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Record W4298147668 · doi:10.32920/ryerson.14648031.v2

Too hot for the Library: access to films with sensitive content

2022· preprint· en· W4298147668 on OpenAlexaff
O. Andrea Wong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
FundersNational Endowment for the Arts
KeywordsCollection developmentCenter (category theory)The artsLibrary scienceContent (measure theory)Principal (computer security)Political scienceSociologyMedia studiesComputer scienceArtVisual artsComputer security

Abstract

fetched live from OpenAlex

This thesis explores decisions on access to collections with sensitive content through a case study analysis of the library principles and archival practices applied to the films from the Youth Film Distribution Center (YFDC). These films are overseen by the Reserve Film and Video Collection at The New York Public Library for the Performing Arts, Dorothy and Lewis B. Cullman Center. The Reserve Film and Video Collection has been the principal circulating audiovisual department for The New York Public Library since the 1950s. The objective of this thesis is to explore processing decisions for films with sensitive content (e.g. films promoting negative stereotypes of their subjects or featuring violent or sexually explicit content). The thesis offers an historical overview of the Youth Film Distribution Center and outlines the processing decisions surrounding levels of access for the YFDC title Seeing (1972).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0090.005
Scholarly communication0.0140.011
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.099
GPT teacher head0.335
Teacher spread0.235 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Qualitative
Domainnot available
GenreOther · Empirical

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
Published2022
Admission routes1
Has abstractyes

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