standOff, and the article is at its publisher via the link below. 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 WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below.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 An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason.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.| second reading | alternatives | an independent re-reading of a facet, recorded whether or not it agrees |
| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| phenomenon | phenomenon | the effect or regularity under study |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| transfer | transfers | a method or concept carried in from another field |
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 1 of 3 relations here carries that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| claim_type | method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_type | the paper's words | |
| phenomenon | topic modelling of document collections Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | phenomenon | the paper's words | |
| method | system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: system-or-tool-building | method | the paper's words | |
| mechanisms | mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[0] | the tagger's words | |
| limitations | The bag-of-words (exchangeability) assumption can split words that belong to a single phrase across different topics. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: every topic assignment over multi-word names and phrases: THE BAG-OF-WORDS ASSUMPTION ALLOWS WORDS THAT SHOULD BE GENERATED BY THE SAME TOPIC (E.G., 'WILLIAM RANDOLPH HEARST FOUNDATION') TO BE ALLOCATED TO SEVERAL DIFFERENT TOPICS | limitations[0] | the tagger's words | |
| limitations | The text-classification results are presented as preliminary and needing further support. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the text-classification result: ALTHOUGH THESE RESULTS NEED FURTHER SUBSTANTIATION, THEY SUGGEST THAT THE TOPIC-BASED REPRESENTATION ... MAY BE USEFUL AS A FAST FILTERING ALGORITHM | limitations[1] | the tagger's words | |
| limitations | Exact posterior inference in LDA cannot be done; only approximate inference is available. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any topic solution treated as the posterior: EXACT INFERENCE IS INTRACTABLE FOR LDA, and only approximate inference algorithms are available | limitations[2] | the tagger's words | |
| limitations | The authors disclaim any epistemological claim about the variables they call topics. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any interpretation of a fitted topic as a theme or subject: the authors state they use the word topics to exploit text-oriented intuitions and make no epistemological claims about these latent variables beyond their utility in representing probability distributions on sets of words | limitations[3] | 1.0 | the tagger's words |
| limitations | The text vocabulary is stated to be an aid to intuition, and the model is not tied to text. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any humanities reading of this model's output: the authors state the language of text collections is used to guide intuition and that the LDA model is not necessarily tied to text | limitations[4] | 1.0 | the tagger's words |
| limitations | Documents as well as words are assumed exchangeable, which the authors note is less often stated. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any use of a topic model over a corpus with an order that matters, such as a diachronic study: the methods assume documents are exchangeable, so the specific ordering of documents in a corpus can be neglected, which the authors note is less often stated formally | limitations[5] | 1.0 | the tagger's words |
| transfers | the document-and-words structure of latent Dirichlet allocation carried over to collaborative filtering, with a user standing in for a document and the movies chosen standing in for its words Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | transfers[0] | 0.85 | the tagger's words |
| controls | density estimation evaluated by likelihood on a held-out test set, with the corpora treated as unlabelled Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: a model rewarded for memorising the documents it was fitted to · outcome (stated by the tagger, no quotation): held | controls[0] | 0.85 | the tagger's words |
| concepts | latent multinomial variables Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: latent multinomial variables | concepts[4] | the paper's words | |
| concepts | exchangeability Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: exchangeability | concepts[5] | the paper's words | |
| concepts | bag-of-words Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: bag-of-words | concepts[6] | the paper's words | |
| concepts | perplexity Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: perplexity | concepts[7] | the paper's words | |
| concepts | topic Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: topic | concepts[8] | the paper's words | |
| senses | a topic - A LATENT MULTINOMIAL VARIABLE, NAMED TO BORROW A MEANING IT DOES NOT CLAIM: the authors say they call these variables topics in order to exploit text-oriented intuitions, and in the same sentence make no epistemological claims about them beyond representing probability distributions over words; the word appears about a hundred times in the body and the disclaimer once, in a footnote Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[0] | 1.0 | the tagger's words |
| senses | the language of text collections - A GUIDE TO INTUITION, NOT A DOMAIN: words, documents and corpora are used throughout because they help intuition, while the model itself is stated not to be necessarily tied to text and is applied to collaborative filtering and other collections Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[1] | 1.0 | the tagger's words |
| senses | exchangeability - TWO ASSUMPTIONS, ONE OF THEM USUALLY LEFT UNSAID: words within a document are exchangeable, which is the bag-of-words assumption everyone states, and DOCUMENTS are exchangeable too, which the authors say is less often stated formally - the second is what removes time from a corpus Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[2] | 1.0 | the tagger's words |
| alternatives | claim_type: method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. a second reading reaching the same value. Recorded: method — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.claim_type | 0.9 | the tagger's words |
| alternatives | method: probabilistic-modelling Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. a second reading DIFFERING from the recorded value. Recorded: system-or-tool-building — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.method | 0.75 | the tagger's words |
| alternatives | phenomenon: latent topic structure in collections of discrete data such as text corpora Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. a second reading DIFFERING from the recorded value. Recorded: topic modelling of document collections — note: A BLIND second reading by the project on 2026-08-25: the record's own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is)., | alternatives.phenomenon | 0.9 | the tagger's words |
| claim_relations | builds-on → hierarchical Bayesian modeling Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[0] | the tagger's words | |
| claim_relations | contradicts → LSI (latent semantic indexing) methodology Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[1] | the tagger's words | |
| claim_relations | extends → Hofmann (1999) probabilistic latent semantic indexing (pLSI) model Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[2] | the tagger's words | |
| claim_relations | contradicts → Hofmann's pLSI treatment of documents Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. differs in: THE PARAMETER IS THE LEVEL AT WHICH THE MODEL IS PROBABILISTIC. The span: 'While Hofmann's work is a useful step toward probabilistic modeling of text, it is incomplete Quotation withheld.' The contradiction is not global - pLSI is granted as a useful step - and it turns on one named thing. | claim_relations[3] | the tagger's words | |
| claim_relations | builds-on → de Finetti (1990) representation theorem for exchangeable random variables Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[4] | the tagger's words | |
| claim_relations | builds-on → Nigam et al. (2000) mixture of unigrams model Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[5] | the tagger's words | |
| concept_edges | topic —operationalised-as→ latent multinomial variables Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: topic · latent multinomial variables | concept_edges[0] | the tagger's words | |
| concept_edges | bag-of-words —same-as→ exchangeability Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: bag-of-words · exchangeability | concept_edges[1] | the tagger's words | |
| concept_edges | bag-of-words —renamed-across-fields→ exchangeability Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. the relation itself is separately evidenced · strength of the relation: 1 · note: ADDS the better-typed edge beside the record's 'same-as'. The same-as is defensible and is left alone; this one says WHY they are the same - it is one assumption renamed between fields. · the relation stated: ONE assumption under two field vocabularies - information retrieval calls it bag-of-words, probability theory calls it exchangeability also in: bag-of-words · exchangeability | concept_edges[2] | the tagger's words | |
| content_tags | LDA is a generative probabilistic model for text corpora: a three-level hierarchical Bayesian model in which each item is a finite mixture over an underlying set of topics. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[0] | the tagger's words | |
| content_tags | Inference uses efficient approximate variational methods and an EM algorithm; results in document modeling, text classification and collaborative filtering are compared with a mixture-of-unigrams model and probabilistic LSI. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[1] | the tagger's words | |
| content_tags | latent Dirichlet allocation (LDA) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: latent Dirichlet allocation (LDA) | content_tags.definitions[0] | the tagger's words | |
| content_tags | topic Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: topic | content_tags.definitions[1] | the tagger's words | |
| content_tags | tf-idf scheme Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE RECOVERED FROM THE PAPER'S OWN CHARACTERS. The recorded quote was LETTERS-ONLY IDENTICAL to this stretch of the staged text but not an exact substring of it - spaces, hyphens, ligature glyphs or mathematical operators differed, so it could never anchor. This is the file's own text between the same two letters: not produced by a rule about the damage, copied out of the file. Round-trip checked (the recovered span is letters-only equal to the original) and length-checked. The text as originally recorded is kept in evidence as extracted. also in: tf-idf scheme | content_tags.definitions[2] | the tagger's words | |
| content_tags | Bayesian Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a unicode and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: Bayesian | content_tags.statistics[0] | the tagger's words | |
| content_tags | mean Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mean | content_tags.statistics[1] | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.