Seeing for ourselves: when teachers make documentary films.<i>Rebel Goulash</i>and<i>Over the Rainbow</i>
Bibliographic record
Abstract
To begin … a moral tale for anthropologists, a fantasy in which an anthropological cinema exists—not documentaries about ‘anthropological’ subjects but films designated by anthropologists to communicate anthropological insights. It is a well‐articulated genre distinct from the conceptual limitations of realist documentary and broadcast journalism. It borrows conventions and techniques from the whole of cinema—fiction, documentary, animation and experimental … this fantasy is more like science fiction than anything else. It is not remotely close to being realized. But it is an ideal worth pursuing (Ruby, 2000 Ruby J 2000 Picturing culture: explorations of film and anthropology (Chicago and London, University of Chicago Press) [Google Scholar], pp. 1–2).
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".