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Lack of Collections as Data

2024· article· en· W4404573117 on OpenAlexaffvenueabout
Christine Smith

Bibliographic record

VenueCanadian Journal of Information and Library Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicWeb Data Mining and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsData scienceComputer science

Abstract

fetched live from OpenAlex

Streaming media licensing by academic libraries has, in recent decades, grown in breadth and complexity. Simultaneously, in the communication and media studies community, critical analysis of consumer-direct streaming media or over-the-top (OTT) platforms has explored media ownership and power dynamics. This article intends to bridge the gap between these two worlds and reframe the discourse of research into modern library analysis, turning away from the “how” media gatekeeping is being experienced by academic librarians and towards “what” media is being withheld. The analysis will be done through a case study of a collection, or rather, absence of collection, of films requested but unavailable for institutional licensing in a specific Canadian academic library. In exploring request, film, and market availability data for each title and analyzing trends found therewithin, the article aims to bring to light possible implications to the future of academia as technological advancements and media powerholders render certain audiovisual cultural artifacts inaccessible.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.020
Open science0.0010.000
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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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