Re-energizing VHS Collections, Expanding Knowledge: A Conversation about VHS Archives
Bibliographic record
Abstract
Scholars, activists, researchers, and artists of a certain age and inclination are burdened with a soon-to-be-obsolete but always-beloved, carefully tended but perhaps recently quieted collection that most likely sits on an office shelf gaining dust: their VHS Archive. Not a personal collection, but a professional one of continuing or even growing value if not usability, this archive has been lovingly built and used, probably over decades, for teaching and research and in support of the movements and issues that have mattered most to the collector. With the help of an Open Education Resources grant from CUNY we built an online teaching resource for a graduate course that would focus on just twelve of these tapes. We hope that the course and its lasting website asks, and will offer some answers about, best practices for reactivating knowledge that might be endangered due to medium obsolescence, and other broader cultural factors of forgetting.
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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.040 | 0.058 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".