Bibliographic record
Abstract
ABSTRACTIn discussions of non-fiction film-making, the issue of performance has often been given short shrift. This article begins to fill this gap by outlining a framework for understanding and discussing the documentary actor's work. I contend that a three-tiered model, which takes into account everyday performative activity (tier #1), the impact of the camera (tier #2) and the influence of specific documentary film frameworks (tier #3), is necessary to describe the non-fiction subject's work effectively. This kind of multifaceted conception also suggests the necessity of a complex, interdisciplinary method of analysis. If one is to consider adequately the nature and implications of documentary ‘acting’, one must draw from and combine the insights of fields that investigate each of the performative levels that non-fiction subjects negotiate. By amalgamating the relevant work of sociologists and social psychologists, film acting scholars and documentary theorists, I enumerate many of the performance stra...
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".