How We Do Things with Words: Analyzing Text as Social and Cultural Data — TEI-tagged view

Dong Nguyen, Maria Liakata, Simon DeDeo, Jacob Eisenstein · 2020 · digital-humanities · doi:10.3389/frai.2020.00062 · full text at the source · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. faceted 2026-08 by reading the locally held text (xml); every evidence string verified as an exact substring of that text before merging. Non-empirical guidance article; no data or results of its own, so replication status is untested. It repeatedly uses Chandrasekharan et al. (2017) on Reddit hate speech as a running worked example rather than as a claim it relates itself to, so that is not recorded as a claim_relation. Discipline is a compromise: the four authors write from NLP, digital humanities, and social science simultaneously ('given our diversity of disciplinary backgrounds'), and the facet cannot express that plurality. Method 'practice-report' was chosen over 'review' because the article reports the authors' own research practice rather than surveying a literature. 2026-08-12, 2026-08-26 and 2026-08-28 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node. Update, 15 September 2026: EXCEPTION, measured with the merge gate's own predicate (Paper.locate): 3 quote(s) on this record are not found as one exact string in the current staged text - content_tags.findings[1].evidence, content_tags.findings[2].evidence, content_tags.statistics[0].evidence. The sentence above does not hold for them as written. Where a row records why - the quote is grounded in another copy of the paper, joins two passages with an ellipsis, or differs from the staged text by extraction noise - that note stays on the row.
3 quotes on this page were cut from a different copy of this article. This project holds two extractions of this paper: the plain text every other quote here is anchored in, and the publisher’s own HTML edition, which we are not licensed to republish. They are verbatim in the second and cannot be found in the first — not because the tagging is wrong, but because the extraction damaged the passage: a page number dropped into the middle of a number, a formula moved out of its sentence, a ligature where two letters belong. The record names the copy for each one. The quote is the paper’s; the extraction is not.
Other fields define these differently the same word, defined another way in another discipline
metadata 7 fields · reliability 5 fields · ground truth 5 fields · sample-size 5 fields · gold standard 4 fields · privacy 3 fields · and 7 more →
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
text as data natural language processing7 annotation5 measurement validity computational social science background concept systematized concept operationalization2 conceptual validity content validity4 ground truth10 gold standard11 reliability12 validity6 annotation scheme2 inter-annotator agreement2 Krippendorff's alpha close reading4 ambiguity3 thick hate speech deductive crowdsourcing topic modeling3 unobservable insight driven automating tasks Domain experts dual use2 Born-digital data unobtrusive measurements privacy8 provenance3 black box APIs data biases spurious results generalizability5 “comparison” set matching design control group3 counterfactual scenario metadata9 Discretization classification scheme Supervised learning15 unsupervised learning5 natural structure unit of text unit of analysis2 interpretability9 human coders annotation examples expert annotators intra -annotator agreement Disagreement between annotators racial bias2 pre-processing steps OCR errors Tokenization Multi-word terms normalization9 word pieces stoplist Dictionaries domain-specific dictionaries spurious features post-hoc explanations Topic models3 number of topics stratified random sample2 synthetic data3 explanatory power predictive performance2 opportunities for interpretation qualitative analyses working across disciplines qualitative insights reliable results2 reproducibility efforts needs of the user Computational text analysis models that are already somewhat interpretable practice-report11

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenoncomputational text analysis of social and cultural concepts— “In this article we describe our experiences with computational text analysis involving rich social and cultural concepts.”
claim-typemethod— “Second, we hope to provide a set of key questions that can guide work with thick social and cultural concepts.”
method — evidence— “In this article we have consolidated our experiences, as scholars from very different disciplines, in analyzing text as social and cultural data and described how the research process often unfolds.”
object of study — evidence— “In this article, we take the reader through a typical research process that involves measuring social or cultural concepts using computational methods, discussing both the opportunities and complications that often arise.”
transferfrom political science: Adcock and Collier's background/systematized concept distinction and their notion of content validity, imported to discipline the conceptualization step of computational text analysis— “If we want to move beyond the use of ad hoc definitions, it can be useful to distinguish between what political scientists Adcock and Collier ( 2001 ) call the “background concept” and the “systematized concept.””transfers[0]
transferfrom literary studies: Close reading carried into computational work not as a rival but as a validation step that informs the model— “Model outputs tell us something about the texts, while a close reading of the texts alongside those outputs tells us something about the models that can be used for more effective model building.”transfers[1]
mechanismA concept must be narrowed from the full range of meanings it carries to the single formulation a study adopts, and those are two different objects with two different names— “The background concept comprises the full and diverse set of meanings that might be associated with a particular term.”mechanisms[0]
mechanismComputation earns its place where patterns exist only in aggregate and are invisible in any single case— “Moreover, many patterns are not obvious in any specific context, but only stand out in the aggregate.”mechanisms[1]
mechanismWhere there is no objective standard, accuracy against annotations measures consistency rather than correctness— “Accuracy, as well as other measures such as precision, recall and F-score, are sometimes presented as a measure of validity, but if we do not have a genuinely objective determination of what something is supposed measure—as is often the case in text analysis—then accuracy is perhaps a better indication of reliability than of validity.”mechanisms[2]
mechanismInter-annotator reliability differs from accuracy in taking no coder as correct and discounting agreement expected by chance— “In contrast, when the reliability is measured based on the output of different annotators, no coder is taken as the standard and the likelihood of coders reaching agreement by chance (rather than because they are “correct”) is factored into the resulting statistic.”mechanisms[3]
mechanismValidation without a gold standard proceeds by sampling across the full range of scores and asking whether each case makes sense under the adopted definition— “That is, take a stratified random sample, selecting observations from the full range of scores, and ask: Do these make sense in light of the systematized concept?”mechanisms[4]
mechanismReading the texts beside the model output informs the model as much as the model informs the reading— “Model outputs tell us something about the texts, while a close reading of the texts alongside those outputs tells us something about the models that can be used for more effective model building.”mechanisms[5]
mechanismAnnotator disagreement can be a property of the concept rather than a defect of the annotators, and can be carried forward instead of resolved— “Disagreement itself can be meaningful and can be integrated in subsequent analyses (Aroyo and Welty, 2013 ; Demeester et al., 2016 ).”mechanisms[6]
mechanismA METHODS GUIDE THAT MAKES `FIND THE COMPETING DEFINITIONS` A REQUIRED STEP, IN A CHECKLIST: `Conceptualization: What is this all about? What are the core concepts you are addressing? And are you being true to their core meaning? What are competing definitions? Which is best suited to the task and why? Does the systematized concept you've selected reflect an adequate understanding of the background concept? How do domain experts approach the topic?` This is Adcock and Collier's vocabulary turned into a procedure a researcher is told to follow before touching data - and it is the closest thing in the corpus to a specification of what this project does.— “Conceptualization: What is this all about? What are the core concepts you are addressing? And are you being true to their core meaning? What are competing definitions? Which is best suited to the task and why? Does the systematized concept you've selected reflect an adequate understanding of the background concept? How do domain experts…mechanisms[7]
builds-on natural language processing (computational text methods)— “the backbone of natural language processing”claim_relations[0]
builds-on Bender and Friedman (2018), Gebru et al. (2018) on dataset documentation and Mitchell et al. (2019) on model documentation— “These questions complement recent work providing guidance and suggestions about datasets (Bender and Friedman, 2018 ; Gebru et al., 2018 ) and models (Mitchell et al., 2019 ) more specifically.”claim_relations[1]
critiques the gold-standard framing of evaluation when applied to contested social and cultural concepts— “However, the notion of ground truth is uncommon in the humanities and social sciences and it is often taken too far in machine learning.”claim_relations[2]
extends Adcock and Collier (2001) on measurement validity in political science— “If we want to move beyond the use of ad hoc definitions, it can be useful to distinguish between what political scientists Adcock and Collier ( 2001 ) call the “background concept” and the “systematized concept.””claim_relations[3]
supports Piper's account of validation as further discovery in two directions— “Applying this circular, iterative process to 450 18th-century novels written in three languages, Piper was able to uncover a new form of “ conversional novel ” that was not previously captured in “ literary history's received critical categories ” (Piper, 2015 , p. 92).”claim_relations[4]
extends the datasheets and model-cards line of work on documenting datasets and models— “These questions complement recent work providing guidance and suggestions about datasets (Bender and Friedman, 2018 ; Gebru et al., 2018 ) and models (Mitchell et al., 2019 ) more specifically.”claim_relations[5]
senseconcept - BACKGROUND: everything the term has meant across the literature, contested and changing— “The background concept comprises the full and diverse set of meanings that might be associated with a particular term.”senses[0]
senseconcept - SYSTEMATIZED: the one definition this study adopts, chosen for its question and framework— “That definition, in turn, represents the systematized concept: the formulation that is adopted for the study.”senses[1]
senseaccuracy - as VALIDITY: what it is usually presented as, and what it cannot be without an objective standard— “Accuracy, as well as other measures such as precision, recall and F-score, are sometimes presented as a measure of validity, but if we do not have a genuinely objective determination of what something is supposed measure—as is often the case in text analysis—then accuracy is perhaps a better indication of reliability than of validity.”senses[2]
senseaccuracy - as RELIABILITY: consistency against a human sample, which is what it actually measures when no ground truth exists— “In NLP and machine learning, most models are primarily evaluated by comparing the machine-generated labels against an annotated sample.”senses[3]
senseambiguity - in computational linguistics: a problem to be solved, on the assumption that one correct interpretation exists— “In Computational Linguistics [.] ambiguity is almost uniformly treated as a problem to be solved; the focus is on disambiguation, with the assumption that one true, correct interpretation exists .”senses[4]
senseambiguity - in literary criticism: prized, alongside interpretation and argumentation, above definitive conclusions— “Kirschenbaum ( 2007 , p. 1) notes that in literary criticism and the digital humanities more broadly “ interpretation, ambiguity, and argumentation are prized far above ground truth and definitive conclusions."”senses[5]
senseannotator disagreement - as NOISE: a failure of the scheme or the coders, to be reduced— “Disagreement between annotators can signal weaknesses of the annotation scheme, or highlight the inherent ambiguity in what we are trying to measure.”senses[6]
senseannotator disagreement - as SIGNAL: a property of the concept, which can be carried into the analysis rather than resolved away— “Disagreement itself can be meaningful and can be integrated in subsequent analyses (Aroyo and Welty, 2013 ; Demeester et al., 2016 ).”senses[7]
senseInsight-driven analysis - describes or explains a phenomenon rather than optimizing a prediction task— “Computational analysis of text motivated by these questions is insight driven: we aim to describe a phenomenon or explain how it came about. For example, what can we learn about how and why hate speech is used or how this changes over time? Is hate speech one thing, or does it comprise multiple forms of expression? Is there a clear boundary…senses[8]
senseDual use - a protective hate-speech method may also be adapted to suppress marginalized speech or dissent— “Questions about potential “dual use” may also arise. Returning to our introductory example, Chandrasekharan et al. ( 2017 ) started with a deceptively simple question: if an internet platform eliminates forums for hate speech, does this impact hate speech in other forums? The research was motivated by the belief that a rising tide of online…senses[9]
senseBorn-digital evidence - massive and unobtrusive, yet ethically constrained by contextual privacy and absent consent— “The use of born-digital data raises ethical concerns. Although early studies often treated privacy as a binary construct, many now acknowledge its complexity (danah boyd and Crawford, 2012 ). Conversations on private matters can be posted online, visible for all, but social norms regarding what should be considered public information may…senses[10]
senseBlack-box collection - commercial APIs can introduce unknown biases into research inference— “Working with data that someone else has acquired presents additional problems related to provenance and contextualization. It may not always be possible to determine the criteria applied during the creation process. For example, why were certain newspapers digitized but not others, and what does this say about the collection? Similar…senses[11]
senseRestricted generalization - biased data can support within-population comparison when claims stay within that populationsenses[12]
senseCounterfactual control - matched untreated forums estimate what would have happened without platform closuresenses[13]
senseDiscretization tradeoff - categories facilitate nonlinear analysis but overly broad bins threaten validity
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senses[14]
senseSupervised versus unsupervised learning - labels learned from annotated data versus structure learned from unlabeled data
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senses[15]
senseAnnotation disagreement - can reveal either scheme weaknesses or inherent ambiguity and may itself be retained as signal
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senses[16]
sensePreprocessing provenance - operationally consequential choices should be documented like editorial or biomedical protocols
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senses[17]
senseTokenization - seemingly basic segmentation that can fail on creative orthography and transform multi-word patterns
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senses[18]
senseWord-piece segmentation - effective for prediction but potentially unsuitable when linguistic-unit interpretation matters
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senses[19]
senseDomain dictionary construction - automation proposes candidates and human theory filters spurious features
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senses[20]
sensePost-hoc explanation - interprets individual predictions without guaranteeing fidelity to the underlying model
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senses[21]
senseTopic count - a user-selected analytical scale like map scale or microscope magnification, not an intrinsic truth
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senses[22]
senseContent revalidation - jointly interrogates concept, indicators, and scores and may force recoding or method replacement
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senses[23]
senseComputational and qualitative analysis - complementary scales for trends and individual stories
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senses[24]
edgegold standard —same-as→ ground truth
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concept_edges[2]
auditground truth —distinct-from→ unobservable — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span calls some concepts fundamentally unobservable; distinct-from ground truth is inferred.
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concept_edges[3]
auditreliability —distinct-from→ validity — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span defines reliability only; the contrast with validity is not in it.
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concept_edges[4]
auditinter-annotator agreement —operationalised-as→ reliability — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span glosses inter-annotator agreement as 'intercoder reliability'; operationalised-as is loose.
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concept_edges[5]
auditKrippendorff's alpha —operationalised-as→ inter-annotator agreement — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: the span answers 'how inter-annotator agreement will be measured' (previous sentence); operationalised-as is loose.
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concept_edges[6]
auditcontent validity —operationalised-as→ annotation scheme — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'the codebook'; 'annotation scheme' is a variant, and operationalised-as is loose.
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concept_edges[7]
auditclose reading —operationalised-as→ validity — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says validation takes the form of close reading for some; the direction of operationalised-as is reversed.
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concept_edges[8]
weightclose reading —operationalised-as→ validity — 0.9
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concept_edges[8]
auditambiguity —contrasted-with→ ground truth — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but attributed: the span quotes Kirschenbaum.
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concept_edges[9]
auditdeductive —part-of→ annotation scheme — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span describes deductive coding rules; part-of the annotation scheme is inferred.
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concept_edges[11]
noteinsight driven —contrasted-with→ automating tasks — The article explicitly contrasts insight-driven social inquiry with task automation and prediction.
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concept_edges[14]
auditdata biases —causes→ spurious results — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says data biases MAY lead to spurious results; hedged.
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concept_edges[15]
notedata biases —causes→ spurious results — The paper says biases may lead to spurious results; weight reflects 'may'.
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concept_edges[15]
weightdata biases —causes→ spurious results — 0.5
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concept_edges[15]
noteSupervised learning —distinct-from→ unsupervised learning — The contrast is labeled data versus unlabeled data.
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concept_edges[16]
for which uselimitation — any computational operationalisation of a rich concept: it raises CONCERNS ABOUT CONCEPTUAL VALIDITY AND MAY LEAD TO SHALLOW OR OBVIOUS CONCLUSIONS
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limitations[0]
for which uselimitation — the guidance this paper offers: OUR GUIDANCE IS BASED ON OUR OWN EXPERIENCES AND IS THEREFORE INHERENTLY IMPERFECT
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limitations[1]
for which uselimitation — the set of guiding questions: WE PROVIDE A SET OF QUESTIONS, THOUGH UNAVOIDABLY INCOMPLETE, THAT CAN SERVE AS A GUIDE FOR THINKING THROUGH CHALLENGES IN EACH STEP OF THE RESEARCH PROCESS
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limitations[2]
for which uselimitation — replacement of other methods, named as the use it does not support: COMPUTATIONAL TEXT ANALYSIS IS NOT A REPLACEMENT FOR BUT RATHER AN ADDITION TO THE APPROACHES ONE CAN TAKE TO ANALYZE SOCIAL AND CULTURAL PHENOMENA USING TEXTUAL DATA
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limitations[3]
duplicate oflimitation — limitations[1]
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limitations[4]
for which uselimitation — the same scope as limitations[1] - the guidance, from experience and INHERENTLY IMPERFECT
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limitations[4]
for which uselimitation — validation of any social or cultural concept measure: FOR SOCIAL AND CULTURAL CONCEPTS THERE IS NO ABSOLUTE GROUND TRUTH
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limitations[5]
for which uselimitation — every measured construct in the paper and the genre: OPERATIONALIZATIONS ARE NEVER PERFECT TRANSLATIONS, AND ARE OFTEN REFINED OVER THE COURSE OF AN INVESTIGATION
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limitations[6]
for which uselimitation — validation in the humanities and social sciences specifically: MANY RELEVANT CONCEPTS ARE FUNDAMENTALLY UNOBSERVABLE, SUCH AS LATENT TRAITS OF POLITICAL ACTORS - unlike tasks such as SPAM FILTERING
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limitations[7]
for which uselimitation — any human-labelled ground truth: WHEN THE GROUND TRUTH COMES FROM PEOPLE, IT MAY BE INFLUENCED BY IDEOLOGICAL PRIORS, PRIORS, PRIMING, SIMPLE DIFFERENCES OF OPINION OR PERSPECTIVE
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limitations[8]
for which uselimitation — the paper's own linear presentation of the research process: THE RESEARCH PROCESS SOUNDS MORE OR LESS LINEAR THIS WAY, BUT EACH OF THESE PHASES OVERLAPS, AND IN SOME INSTANCES REQUIRES US TO RETURN TO PREVIOUS STEPS
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limitations[9]
for which uselimitation — conclusions drawn from an operationalisation: CHOICES REGARDING HOW TO OPERATIONALIZE AND ANALYZE THESE CONCEPTS CAN RAISE SERIOUS CONCERNS ABOUT CONCEPTUAL VALIDITY AND MAY LEAD TO SHALLOW OR OBVIOUS CONCLUSIONS
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limitations[10]
weightDomain experts → qualitative insights — 0.7
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unhoused_relations[1]
weightblack box APIs → data biases — 0.7
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unhoused_relations[2]
weightDiscretization → validity — 0.7
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unhoused_relations[5]
weightunit of text → unit of analysis — 0.7
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unhoused_relations[6]
weightexpert annotators → reliable results — 0.7
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unhoused_relations[7]
weightpost-hoc explanations → models that are already somewhat interpretable — 0.5
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unhoused_relations[9]
weightexplanatory power → predictive performance — 0.7
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unhoused_relations[11]
weightqualitative analyses → Computational text analysis — 0.7
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unhoused_relations[12]
weightTokenization → Multi-word terms — 0.7
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unhoused_relations[13]
replicationuntested
no type for thisBorn-digital data → unobtrusive measurements — Because born-digital sources do not require direct elicitation, they enable unobtrusive measurement. Enables is absent from the governed types, and the gate correctly refused a causes edge without its governed effect cue.
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unhoused_relations[0]
no type for thisDomain experts → qualitative insights — Domain experts identify qualitative insights that can strengthen computational analysis and enrich the answer. Advises/contributes is not a governed relation type.
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unhoused_relations[1]
no type for thisblack box APIs → data biases — Closed commercial APIs are likely to introduce biases whose form and severity researchers cannot inspect. Introduces-risk-of is not a governed type, and the wording is probabilistic.
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unhoused_relations[2]
no type for thismatching design → control group — The matching design populates the control group with untreated forums similar to the treated ones. Constructs/selects is absent from the governed types.
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unhoused_relations[3]
no type for thiscontrol group → counterfactual scenario — The matched control group is used to estimate what would have happened without the closures. Estimates-counterfactual is absent and should not be forced into operationalised-as without its governed cue.
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unhoused_relations[4]
no type for thisDiscretization → validity — Overly broad discretized categories can threaten validity. Threatens is absent from the governed types, and deterministic causation would overstate 'can'.
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unhoused_relations[5]
no type for thisunit of text → unit of analysis — The labeled unit of text can differ from the final unit of analysis. Differs-in-level-from is not captured by the governed identity or distinction types.
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unhoused_relations[6]
no type for thisexpert annotators → reliable results — Highly trained or expert annotators tend to produce more reliable results for complex concepts. Suitable-for/tends-to-improve is absent and explicitly hedged.
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unhoused_relations[7]
no type for thispre-processing steps → reproducibility efforts — Preprocessing choices have a large impact on reproducibility efforts. Affects is absent from the governed relation types.
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unhoused_relations[8]
no type for thispost-hoc explanations → models that are already somewhat interpretable — Post-hoc explanations are an alternative to restricting analysis to models that are already somewhat interpretable, but may be unfaithful. Alternative-to-with-fidelity-risk is absent.
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unhoused_relations[9]
no type for thisnumber of topics → needs of the user — The appropriate topic count is determined by the needs of the user rather than the collection. Determined-by-analytical-purpose is absent from the governed types.
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unhoused_relations[10]
no type for thisexplanatory power → predictive performance — For insight-driven studies, improving explanatory power can be more useful than small predictive-performance gains. Preferred-over-for-purpose is absent from the governed types.
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unhoused_relations[11]
no type for thisqualitative analyses → Computational text analysis — Moving between large-scale computational text analysis and small-scale qualitative analysis combines their strengths. Complements is absent from the governed types.
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unhoused_relations[12]
no type for thisTokenization → Multi-word terms — Deciding whether multi-word terms are tokenized as one unit can dramatically alter textual patterns. Treatment-of is absent and causation would misstate the relevant agent.
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unhoused_relations[13]
limitationoperationalizing rich concepts computationally can raise conceptual-validity concerns and yield shallow conclusions
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limitations[0]
limitationThe guidance is drawn from the authors' own experience and is explicitly acknowledged as imperfect.
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limitations[1]
limitationThe proposed set of guiding questions is acknowledged to be incomplete.
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limitations[2]
limitationThe authors position computational text analysis as an addition to, not a substitute for, other analytic approaches — i.e. it cannot answer such questions alone.
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limitations[3]
limitationThe guidance is offered as the authors' own experience and explicitly imperfect
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limitations[4]
limitationFor social and cultural concepts there is no ground truth to appeal to, and definitions are contested and change
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limitations[5]
limitationAn operationalisation is never a faithful rendering of the concept it stands for
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limitations[6]
limitationMany of the concepts at issue cannot be observed at all, which is what makes validation hard rather than merely laborious
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limitations[7]
limitationHuman labels are not neutral either - they carry the annotators' priors and perspectives
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limitations[8]
limitationThe research process is presented linearly but is not, and later stages feed back into the question itself
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limitations[9]
limitationA poor operationalisation does not merely add noise - it can produce conclusions that are shallow rather than wrong
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limitations[10]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingA human reader is better equipped than a computer to make logical inferences, resolve ambiguities and apply cultural knowledge.
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content_tags.findings[0]
findingThe notion of ground truth is uncommon and risky in the humanities/social sciences: labels often mean something other than expected, threatening conceptual validity.
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content_tags.findings[1]
not machine-vouchedfinding — declared-ellipsis - An elided quote: the quote marks its cuts with an ellipsis, and each of its 2 pieces is in our copy of the paper, some differing slightly from our copy in letters, notation or a small word ('the notion of ground truth is uncommon i...'; 'using them as a “ground truth” requires ...'). The joined string is in no copy.content_tags.findings[1]
findingInterdisciplinary collaboration is essential to realize the potential of text-as-data analysis.
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content_tags.findings[2]
not machine-vouchedfinding — not-in-our-copy - The quote is in the original web page we fetched for this paper, but not in the text we tag against, which was extracted differently. Checked with ligatures, hyphens, spacing and punctuation set aside.content_tags.findings[2]
findinga concept often has several valid definitions, and definitions may be contested over time
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content_tags.findings[3]
findingvalidity then has to be judged by other techniques
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content_tags.findings[4]
findingthis kind of validation is rare in NLP, yet matters most when it is hard to tell what drives a model
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content_tags.findings[5]
findingdisagreement between annotators can reveal weaknesses in the annotation scheme or the inherent ambiguity of what is being measured
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content_tags.findings[6]
findingannotation carries bias: tweets in African American English, and by self-identified African Americans, were more often labelled offensive
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content_tags.findings[7]
findingclose reading of 450 eighteenth-century novels alongside model output revealed a genre absent from literary history's own categories
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content_tags.findings[8]
quantity0.80 or above — the conventional threshold for acceptable intercoder agreement in communication research
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content_tags.quantities[0]
quantity450 18th-century novels — the corpus in which close reading beside model output found an unrecognised genre
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content_tags.quantities[1]
definitionconceptual validity
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content_tags.definitions[0]
definitionground truth
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content_tags.definitions[1]
definitionbackground concept
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content_tags.definitions[2]
definitionsystematized concept
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content_tags.definitions[3]
definitionreliability
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content_tags.definitions[4]
definitionvalidity
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content_tags.definitions[5]
definitionannotation scheme
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content_tags.definitions[6]
definitioncontent validity
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content_tags.definitions[7]
key-termstext, computational, social, humanities, annotation, validity, measurement, interpretation, corpus, background concept, systematized concept, operationalization, conceptual validity, content validity, ground truth, gold standard, reliability, annotation scheme, inter-annotator agreement, Krippendorff's alpha, close reading, ambiguity, thick, unobservable
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptground truth — not defined in this paper's own words; the assumed correct answer, which the paper argues does not exist for contested concepts
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concepts[10]
conceptgold standard — not defined in this paper's own words; its machine-learning name, and the frame this paper says is often taken too far
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concepts[11]
concepthate speech — not defined in this paper's own words; the running example of a contested concept with an external definition used as an anchor
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concepts[20]
conceptinsight driven — not defined in this paper's own words; analysis aimed at describing or explaining phenomena rather than merely predicting labels
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concepts[25]
conceptautomating tasks — not defined in this paper's own words; performance-oriented computational work contrasted with theory-led insight generation
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concepts[26]
conceptDomain experts — not defined in this paper's own words; collaborators who revise questions and identify where qualitative knowledge strengthens analysis
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concepts[27]
conceptdual use — not defined in this paper's own words; risk that a method developed for protective purposes can be repurposed for repression
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concepts[28]
conceptBorn-digital data — not defined in this paper's own words; naturally occurring digital sources enabling unobtrusive and large-scale study
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concepts[29]
conceptunobtrusive measurements — not defined in this paper's own words; observation without direct elicitation that might alter participant responses
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concepts[30]
conceptprivacy — not defined in this paper's own words; non-binary ethical property not reducible to explicit visibility settings
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concepts[31]
conceptprovenance — not defined in this paper's own words; origin and creation context that may be inaccessible when reusing acquired data
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concepts[32]
conceptblack box APIs — not defined in this paper's own words; commercial interfaces whose closed collection process can introduce unknown research bias
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concepts[33]
conceptdata biases — not defined in this paper's own words; collection distortions that can create spurious results and constrain inference
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concepts[34]
conceptspurious results — not defined in this paper's own words; findings induced by biased repurposed data rather than the target phenomenon
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concepts[35]
conceptgeneralizability — not defined in this paper's own words; scope of extrapolation limited by language, target, and population biases
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concepts[36]
concept“comparison” set — not defined in this paper's own words; documents outside a core set needed to establish a distinctive property
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concepts[37]
conceptmatching design — not defined in this paper's own words; control-selection strategy matching forums to treated communities
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concepts[38]
conceptcontrol group — not defined in this paper's own words; untreated comparison forums selected to resemble the closed forums
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concepts[39]
conceptcounterfactual scenario — not defined in this paper's own words; estimated state in which the platform did not close the specific forums
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concepts[40]
conceptmetadata — not defined in this paper's own words; information about document creation or reception that can be valuable but error-prone
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concepts[41]
conceptDiscretization — not defined in this paper's own words; conversion of continuous variables into categories that can aid nonlinear analysis but threaten validity
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concepts[42]
conceptclassification scheme — not defined in this paper's own words; category system determining which variations are visible or hidden
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concepts[43]
conceptSupervised learning — not defined in this paper's own words; learning labels from labeled data for inference on unlabeled text
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concepts[44]
conceptunsupervised learning — not defined in this paper's own words; learning from unlabeled data, often for exploration
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concepts[45]
conceptnatural structure — not defined in this paper's own words; patterns uncovered from unlabeled data by unsupervised approaches
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concepts[46]
conceptunit of text — not defined in this paper's own words; segment that receives an annotation or code
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concepts[47]
conceptunit of analysis — not defined in this paper's own words; final inferential level that may differ from the annotated text segment
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concepts[48]
conceptinterpretability — not defined in this paper's own words; capacity to communicate patterns, detect spurious features, and support result interpretation
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concepts[49]
concepthuman coders — not defined in this paper's own words; people who label data for full coding or model training
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concepts[50]
conceptannotation examples — not defined in this paper's own words; worked cases added as annotation guidelines are iteratively updated
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concepts[51]
conceptexpert annotators — not defined in this paper's own words; specialists favored for complex or domain-specific concepts
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concepts[52]
conceptintra -annotator agreement — not defined in this paper's own words; repeatability check within individual coders
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concepts[53]
conceptDisagreement between annotators — not defined in this paper's own words; signal of either scheme weakness or inherent ambiguity that may itself be analytically meaningful
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concepts[54]
conceptracial bias — not defined in this paper's own words; annotation and detection disparity affecting African American English tweets
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concepts[55]
conceptpre-processing steps — not defined in this paper's own words; underreported preparation choices that shape measures, analysis, and reproducibility
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concepts[56]
conceptOCR errors — not defined in this paper's own words; recognition defects whose form varies with source, date, typography, and software
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concepts[57]
conceptTokenization — not defined in this paper's own words; segmentation of character sequences into word-like units with consequential assumptions
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concepts[58]
conceptMulti-word terms — not defined in this paper's own words; multi-token expressions whose treatment as one unit can change discovered patterns
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concepts[59]
conceptnormalization — not defined in this paper's own words; loosely defined grouping of abbreviations, ordinals, and spelling variants
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concepts[60]
conceptword pieces — not defined in this paper's own words; statistical sub-token segments optimized for prediction but not necessarily linguistically meaningful
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concepts[61]
conceptstoplist — not defined in this paper's own words; task-specific vocabulary exclusion list
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concepts[62]
conceptDictionaries — not defined in this paper's own words; transparent category word lists used to code text
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concepts[63]
conceptdomain-specific dictionaries — not defined in this paper's own words; corpus-adapted lexicons that address limits of general-purpose dictionaries
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concepts[64]
conceptspurious features — not defined in this paper's own words; non-target correlations learned from imbalanced or noisy datasets
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concepts[65]
conceptpost-hoc explanations — not defined in this paper's own words; prediction-level interpretations that may fail to faithfully describe the original model
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concepts[66]
conceptTopic models — not defined in this paper's own words; usually unsupervised models constrained for interpretable exploration
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concepts[67]
conceptnumber of topics — not defined in this paper's own words; analytical scale chosen according to user needs rather than an inherent collection value
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concepts[68]
conceptstratified random sample — not defined in this paper's own words; sample across the score range used for qualitative content revalidation
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concepts[69]
conceptsynthetic data — not defined in this paper's own words; constructed observations permitting controlled validation comparisons
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concepts[70]
conceptexplanatory power — not defined in this paper's own words; insight-driven objective sometimes more useful than incremental prediction gains
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concepts[71]
conceptpredictive performance — not defined in this paper's own words; forecasting objective contrasted with explanatory gains
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concepts[72]
conceptopportunities for interpretation — not defined in this paper's own words; disagreements between model output and human labels treated as analytically revealing cases
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concepts[73]
conceptqualitative analyses — not defined in this paper's own words; small-scale inquiry combined iteratively with large-scale computation
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concepts[74]
conceptworking across disciplines — not defined in this paper's own words; interdisciplinary setting in which operational and analytic choices require negotiation
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concepts[75]
conceptqualitative insights — not defined in this paper's own words; domain knowledge added to strengthen computational answers
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concepts[76]
conceptreliable results — not defined in this paper's own words; annotation outcome more often produced by highly trained or expert coders for complex concepts
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[77]
conceptreproducibility efforts — not defined in this paper's own words; ability to reproduce analysis affected by preprocessing choices
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[78]
conceptneeds of the user — not defined in this paper's own words; desired analytical scale that determines the appropriate topic count
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[79]
conceptComputational text analysis — not defined in this paper's own words; large-scale approach treated as a complement to qualitative inquiry
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[80]
conceptmodels that are already somewhat interpretable — not defined in this paper's own words; models restricted to a small number of human-readable features
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[81]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
hypothesis-testing
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[0]
not machine-vouchedhypothesis-testing — not-in-our-copy - The quote is in the original web page we fetched for this paper, but not in the text we tag against, which was extracted differently. Checked with ligatures, hyphens, spacing and punctuation set aside.content_tags.statistics[0]
sample-size n/N
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]
Krippendorff's alpha
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[2]
inter-annotator agreement
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[3]
F-score
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[4]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typemethod — a second reading reaching the same value; recorded: method
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.claim_type
phenomenoncomputational text analysis of social and cultural concepts — a second reading reaching the same value; recorded: computational text analysis of social and cultural concepts
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.phenomenon

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher and is linked, not reproduced. Excerpts shown are short quotations for identification and verification. · back to index · concepts · authors · עברית
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