Interview with Rick Prelinger
Bibliographic record
Abstract
Rick Prelinger wears many hats: he is an archivist and an activist, a writer and a filmmaker; he has preserved the eccentricities and banalities of American cultural heritage and projected them back to the world via both Open Access digital repositories and carefully curated programs of ephemeral and orphaned films. He is perhaps best known as the founder of the Prelinger Archives, a collection of about 60,000 industrial, advertising, educational, and amateur films, which encourage and facilitate not only preservation, but appropriation by allowing free access, downloading and reuse of its materials. For this special issue Sophie Cook, Beatriz Bartolome Herrera, and Papagena Robbins reached Prelinger virtually to talk about his work, bridging the distance between Montreal, Quebec, and Santa Cruz, California, where he currently works as an Associate Professor of Film and Digital Media at the University of California. The text that follows is an edited interview with Prelinger about his films, his work as an archivist, and the establishment of Prelinger Archives.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".