Bibliographic record
Abstract
HIGHLIGHTS FROM THE TORONTO INTERNATIONAL FILM FESTIVAL 2015 The print ads and trailers for Toronto's 40th edition (10-20 September 2015) showed a striking motif of a powdered explosion at reduced speed varying only by colour scheme - a nice metaphor for the celebratory blast of diversity that any healthy entity turning forty ought to enjoy. But far from being complacent, Toronto humbly adapted to a competitive festival circuit by tweaking key strategies (all but retreating from 2014's decision permitting only world premieres to screen on its opening weekend), while also launching new ideas (the inauguration of a juried feature film competition eponymously named after Jia Zhangke's 2000 film Platform). On a related note, Sinophone content dominated this year's skinny selection of about two dozen Asian films, about half of which came from China, Hong Kong and Taiwan. Elsewhere, the Philippines and...
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".