phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | computational analysis of large visual cultural datasets— “These concepts enable computational exploration of both large and small visual cultural data.” | |
| claim-type | method— “I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general.” | |
| method — evidence | — “These concepts do not require formulas to explain, and they can be presented in one article, which is what I will attempt here.” | |
| transfer | from data science / machine learning / computer science: the core analytic concepts of data science (objects, features, feature space, distance, dimension reduction) are imported wholesale as a framework for art-historical and humanistic study— “I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general.” | transfers[0] |
| transfer | from data science: the term "features" is adopted into digital art history in place of the sciences' "variable", and stretched to cover existing metadata as well as computed attributes— “I suggest that in digital art history we adapt the term “features” to refer to both information that can be extracted from objects through computer analysis and the already available metadata.” | transfers[1] |
| mechanism | Because a computer sees only low-level data (pixels, letters) and not meaning, analysts extract many additional features in the hope that together they carry enough information to bridge that gap.— “Trying to “close the semantic gap” (this is the standard phrase in computer science publications) is one of the motivations for using multiple features.” | mechanisms[0] |
| mechanism | Human side-by-side comparison breaks down as collections grow, which is what forces the substitution of computed distance in feature space for perceptual comparison.— “Manual comparison does not scale well for big data.” | mechanisms[1] |
| mechanism | The need to compute by hand constrained the founders of statistics to one- and two-variable methods, and that constraint still shapes how data is imagined today.— “The practicality of manual computation was an important consideration for the people who were consolidating statistics in the beginning of the 20th century.” | mechanisms[2] |
| extends | Franco Moretti, 'Conjectures on World Literature' (2000)— “Moretti’s point certainly applies to all other humanities fields; and it applies even more to the analysis of contemporary culture.” | claim_relations[0] |
| sense | phenomenon - AS INDEPENDENT: something that objectively exists regardless of how we study it, irrespective of which objects and properties we choose— “we could assume that a phenomenon (such as “contemporary society,” for example) objectively exists regardless of how we study it (i.e., what we use as objects and their properties).” | senses[0] |
| sense | phenomenon - AS CONSTITUTED: equal to the set of objects and properties used to study it, so that it is made by its representations and the conversations about it— “That is, a phenomenon is constituted by its representations and the conversations about it.” | senses[1] |
| sense | feature selection - stated as BOTH a limitation and an advantage, because isolating features is what makes comparison on chosen dimensions possible at all— “but also an advantage – by letting us isolate particular features, we can compare artifacts only on dimensions we want.” | senses[2] |
| sense | variable - used in opposite ways by the two fields the paper is joining: one isolated variable for understanding in statistics, many features for automation in data science— “Statistics and quantitative social science that uses it ideally wants to isolate one independent and one dependent variable, because the goal is understanding the phenomenon. Data science wants to use many features in the hope that together they contain the right information for automating recognition, classification, or another cognitive… | senses[3] |
| audit | features —operationalised-as→ feature space — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says each feature BECOMES A DIMENSION of the feature space; operationalised-as is loose - part-of is what is stated.— “To the concepts above (objects and features) we are going to add the third core concept: feature space. We assume that our data is stored in a table. But now we will conceptualize our data table as a geometric space of many dimensions. Each feature becomes one of the dimensions. Each object becomes a point in this space” | concept_edges[1] |
| note | features —operationalised-as→ feature space — The second constituting move, stated as a conceptualisation rather than a discovery: 'we will CONCEPTUALIZE our data table as a geometric space…' Similarity becomes distance because the table is read as a space.— “To the concepts above (objects and features) we are going to add the third core concept: feature space. We assume that our data is stored in a table. But now we will conceptualize our data table as a geometric space of many dimensions. Each feature becomes one of the dimensions. Each object becomes a point in this space” | concept_edges[1] |
| weight | features —operationalised-as→ feature space — 0.9— “To the concepts above (objects and features) we are going to add the third core concept: feature space. We assume that our data is stored in a table. But now we will conceptualize our data table as a geometric space of many dimensions. Each feature becomes one of the dimensions. Each object becomes a point in this space” | concept_edges[1] |
| for which use | limitation — the choice of examples, declared as a specification rather than confessed: SINCE MY ARTICLE IS WRITTEN FOR HUMANITIES AUDIENCE, IT IS ON PURPOSE BIASED - MY EXAMPLES COME FROM HUMANITIES AS OPPOSED TO ECONOMICS OR SOCIOLOGY— “Since my article is written for humanities audience, it is on purpose biased–my examples of the application of the core concepts of data science come from humanities as opposed to economics or sociology.” | limitations[0] |
| for which use | limitation — the two-feature comparison: THIS EXAMPLE DISREGARDS ALL OTHER TYPES OF DIFFERENCE: SUBJECT MATTER, COMPOSITION, COLOR PALETTE, BRUSHWORK— “Note that, of course, this example disregards all other types of difference: subject matter, composition, color palette, brushwork, and so.” | limitations[1] |
| for which use | limitation — any feature-based measure of similarity: EVEN IF WE USE MANY DIFFERENT FEATURES, WE CANNOT BE SURE THAT WE HAVE CAPTURED THE RIGHT INFORMATION TO QUANTIFY DIFFERENCE AS WE HUMANS SEE IT— “Note that just as it was the case with van Gogh example, even if we use many different features, we cannot be sure that we have captured the right information to quantify difference as we humans see it.” | limitations[2] |
| for which use | limitation — any reading of the reduced plots: IT CAN BE QUITE CHALLENGING TO INTERPRET THE MEANING OF EACH DIMENSION once dimension reduction has been applied— “This is the serious challenge of dimension reduction algorithms – while they allow us to represent data using scatterplots where we can see the structure of a space easily, it can be quite challenging to interpret the meaning of each dimension.” | limitations[3] |
| for which use | limitation — any computational result about a cultural phenomenon: the article states the phenomenon must first be represented as a finite set of individual objects and a finite set of their features— “Before we can use computers to analyze a phenomena or activity, it has to be represented as a finite set of individual objects and also a finite set of their features.” | limitations[4] |
| for which use | limitation — a similarity computed over selected image features: the article states it disregards subject matter, composition, colour palette and brushwork, and treats that as both a limitation and an advantage— “this example disregards all other types of difference: subject matter, composition, color palette, brushwork, and so. However, this is not only a limitation” | limitations[5] |
| for which use | limitation — the age, gender and emotion variables in this project: the age and gender estimates came from Amazon Mechanical Turk and the facial features from a commercial recognition service— “Age and gender estimates were obtained by using Amazon Mechanical Turk service.” | limitations[6] |
| replication | untested | |
| limitation | The chosen examples are deliberately biased toward the humanities rather than being a neutral survey of data science.— “Since my article is written for humanities audience, it is on purpose biased–my examples of the application of the core concepts of data science come from humanities as opposed to economics or sociology.” | limitations[0] |
| limitation | A two-feature comparison of images ignores every other kind of difference between the works.— “Note that, of course, this example disregards all other types of difference: subject matter, composition, color palette, brushwork, and so.” | limitations[1] |
| limitation | Even with many features there is no guarantee that the extracted features capture difference as humans perceive it.— “Note that just as it was the case with van Gogh example, even if we use many different features, we cannot be sure that we have captured the right information to quantify difference as we humans see it.” | limitations[2] |
| limitation | Dimension-reduced plots are hard to interpret because the axes no longer correspond to single features.— “This is the serious challenge of dimension reduction algorithms – while they allow us to represent data using scatterplots where we can see the structure of a space easily, it can be quite challenging to interpret the meaning of each dimension.” | limitations[3] |
| limitation | Computational analysis has a precondition that the article says cannot be changed: the phenomenon must first be made into a finite set of objects and a finite set of features.— “Before we can use computers to analyze a phenomena or activity, it has to be represented as a finite set of individual objects and also a finite set of their features.” | limitations[4] |
| limitation | The comparison is confined to the chosen features, and everything else is set aside — which the article names as a limitation and then reframes as an advantage.— “this example disregards all other types of difference: subject matter, composition, color palette, brushwork, and so. However, this is not only a limitation” | limitations[5] |
| limitation | The demographic and facial variables in the featured project were produced by paid crowdworkers and a commercial face-analysis service. | limitations[6] |
| limitation | THE PAPER STATES THAT THE CENTRAL RISK CANNOT BE RETIRED BY ADDING MORE MEASUREMENT: 'even if we use many different features, we cannot be sure that we have captured the right information to quantify difference as we humans see it'. It then declines the obvious repair - 'producing a single correct map should not be our main goal' - so the limitation is accepted as a condition of the method rather than treated as a defect to fix. | limitations[7] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Classical statistics and data science use variables in opposite ways: statistics isolates one independent and one dependent variable in order to explain a phenomenon, while data science piles up many features in order to automate a cognitive task. | content_tags.findings[0] |
| finding | The feature-space concept lets the humanities' oldest method, comparison, be scaled up to very large cultural datasets. 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.findings[1] |
| finding | Representing images in a feature space equates visual difference with distance in that space. 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.findings[2] |
| finding | Automatic classification, probably the most common data-science application, is in the author's view the least interesting one for the humanities. 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.findings[3] |
| finding | Dimension reduction makes the shape of a high-dimensional space visible but at the cost of interpretability, because each plotted axis stands for a combination of features rather than one feature. 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.findings[4] |
| finding | On average brightness and saturation, van Gogh's Paris paintings vary more than his Arles paintings. 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.findings[5] |
| finding | The images popularly associated with Impressionism make up only a smaller part of the artworks these artists created. 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.findings[6] |
| finding | The Instagram tags show little interaction between two types: one group of users used generic tags, another tagged local, specific events. 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.findings[7] |
| quantity | 10.5 million — number of publicly visible Instagram images collected across New York City over five months in 2014 for the On Broadway project 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.quantities[0] |
| quantity | 6,165 / 13,208 / 21,465 (5845 unique) — Instagram users, images and tags (and unique tags) collected in central Kyiv over 17–22 February 2014 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.quantities[1] |
| quantity | 200 — number of visual features the lab extracted from each Impressionist painting image before reducing dimensions 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.quantities[2] |
| quantity | approximately 6000 — size of the French Impressionist painting dataset visualised with PCA 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.quantities[3] |
| quantity | 3200 — number of Instagram self-portraits assembled and made filterable in the Selfiecity interface 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.quantities[4] |
| quantity | 50,000 — size of the random Instagram image sample taken from each of Bangkok and Tokyo for the Phototrails comparison 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.quantities[5] |
| variable | average color saturation (dependent) 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.variables[2] |
| variable | average hue (dependent) 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.variables[3] |
| definition | data science 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.definitions[0] |
| definition | 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. | content_tags.definitions[1] |
| definition | data 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.definitions[2] |
| definition | feature space 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.definitions[3] |
| definition | dimension reduction 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.definitions[4] |
| definition | semantic gap 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.definitions[5] |
| definition | coding 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.definitions[6] |
| definition | supervised learning 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.definitions[7] |
| concept | data 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[0] |
| concept | phenomenon 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[1] |
| concept | 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[2] |
| concept | objects 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[3] |
| concept | feature space 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[4] |
| principal component analysis PCA | 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] |
| multidimensional scaling MDS | as written: multi-dimensional Scaling (MDS) 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] |
| mean | as written: mean hue (angle) and brightness mean 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] |
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.
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