Bibliographic record
Abstract
HIGHLIGHTS FROM THE TORONTO INTERNATIONAL FILM FESTIVAL 2007 With a festival as big as Toronto's, pinning down a general theme about its line-up each year would seem to defy its promise of something-for-everyone, as this year's slate of 349 films from 55 countries showed. Although larger media outlets named discernible themes among the festival's subsections anyway - "frustrated youth" for one; "war in Iraq and global terrorism" for another, the most engaging films this year were apt to be foremost of all, engaging and well-told stories. Here are ten examples: 4 Months, 3 Weeks and 2 Days (Cristian Mungiu, Romania 2007)The waning period of the Ceausescu regime sets the stage for a young woman's labours to arrange a back-alley abortion for her college roommate. Although the illegality of the undertaking furnishes much of the plot's intrigue, the film's gravitas owes much to Mungiu's calibrated direction, which eschews reactionary...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.421 | 0.118 |
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".