More product, less process in film collections: a case study at the Pacific Cinematheque
Bibliographic record
Abstract
This thesis is an applied case study, wherein a donation of materials was assessed at the Pacific Cinematheque Archive between January – June 2019. The archive has struggled to process their backlog due to a strain on resources—an issue that is prevalent in the archival profession, and particularly in small institutions. The objective was to apply broad principles from Mark A. Greene and Dennis Meissner’s 2005 paper “More Product, Less Process: Revamping Traditional Archival processing,” aimed at large paper and manuscript collections, to managing backlog at the Pacific Cinematheque Archive in order to help establish a minimal processing workflow that would allow for faster access to materials in the future. The case study explores the applicability of Greene & Meissner’s processing framework in a small, film-centric archive, as well as the implications of making archival selections in this setting and the importance of preserving and providing access to these films.
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".