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.
Bibliographic record
Abstract
Investigations into format shifts from physical to digital access in libraries often centre print materials. Similarly, recent calls to action for an increasing focus on acquisition of materials that support equity, diversity, and inclusion (EDI) efforts within postsecondary institutions often centre print resources. For academic libraries, media like film have unique access and acquisition models that do not correspond to print and pose unique challenges extending back to the Hollywood studios that create and distribute films. This paper explores the dual shifts in academic libraries toward collecting fewer physical films and collecting more content to support EDI mandates, and asks: first, whether the shift away from collecting physical media may also be a shift away from including diverse perspectives in film collections; and second, if we have the data to draw a measurable and demonstrable conclusion. A comprehensive literature review traces efforts to assess markers of diversity in large library collections and/or film collections over the past two decades and helps establish a methodology that combines analyzing data from the library catalogue and Wikidata. Findings revealed that the completeness and consistency of the data over time makes drawing strong conclusions difficult and demonstrated the challenges of this approach in addressing EDI analysis, even when augmenting catalogue metadata with Wikidata. Curation and choice are perhaps more important in building a diverse film collection than questions of format alone, despite the challenges in assessing and collecting film which is and has always been a format in rapid and continual flux.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it