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Record W2282224467 · doi:10.1075/ssol.5.1.02dix

Judging a book by its cover

2015· article· en· W2282224467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Study of Literature · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSimilarity (geometry)Cover (algebra)Group (periodic table)Information retrievalMeasure (data warehouse)Computer scienceSimilarity measurePsychologyArtificial intelligenceNatural language processingData miningEngineeringImage (mathematics)Chemistry

Abstract

fetched live from OpenAlex

In this experiment, we investigated whether book covers can signal sub-genre information to knowledgeable readers. Self-identified science-fiction fans and mystery fans sorted 80 randomly selected book covers from each of those genres into groups of their own devising. The sorts were used to identify similarity among books, and that similarity structure was used to measure similarity among subjects. Cluster analysis was then used to find groups of subjects who sorted similarly. Linear models were demonstrated that group membership was related to the knowledge subjects reported about the genres. This pattern of results supports the view that book covers constitute an implicit signaling system between publishers and experienced readers of a fictional genre.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.306
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it