Bibliographic record
Abstract
Collections of independent, non-commercial works often represent voices and speak to topics not seen in mainstream media, and they are still often cared for outside of major collecting institutions. Since 2011, activist audiovisual archivists have organized Community Archiving Workshops (CAWs) in the US and beyond to help caretakers of endangered media and film collections jump-start preservation efforts. In the spirit of ‘each one, teach one,’ experienced archivists share skills with other volunteers to inspect and inventory a collection, thus giving caretakers the data they need to select priority works for preservation. CAW organizers are committed to training more people to carry out CAWs in their own communities; a grant-funded project will pilot this approach in partnership with cultural heritage organizations in three regional hubs (Nashville, TN; Madison, WI; and Oakland, CA) beginning in 2018.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".