Bibliographic record
Abstract
SEMANTIC VAGUENESS has been a typical feature of cinema's professional terminology and taxonomy, a problem which exists both in the academic circles of film studies and among film-makers themselves. Moreover, it is amplified by fashionable but meaningless jargon of some film reviewers and commercial newspeak, the intention of which is often disinformation in the first place. Over the past decades, a few attempts have been made to improve this situation through the publication of film term glossaries and dictionaries, including Geduld-Gottesman's An Illustrated Glossary of Film Terms (1973), S. E. Browne's Film-Video Terms and Concepts (1992) and F. Beaver's Dictionary of Film Terms (1994). A major difficulty facing film dictionary writers has been whether to aim at the practical filmmaker or academic theorist; uniting these two user groups has not always been successful. Another problem plaguing some of these reference publications has been that the entries did not go beyond...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".