Bibliographic record
Abstract
TORONTO INTERNATIONAL FILM FESTIVAL 2010 HIGHLIGHTS Having leapt into the exclusive club of industry film festivals of late, the Toronto International Film Festival nevertheless marked a transitional phase this year when it unveiled the Bell Lightbox, a donated piece of downtown real estate that is now home for year-round public programming and events. And in a shift indicative of its ambition, programming for TIFF's 35th edition (9-19 September 2010) hinted at a desire to distinguish itself from a growing pool of international rivals. Though its annual selection of films from Asia is usually conservative, this year's crop of titles was notable for bringing into relief the intricacies of intra-Asia and transnational cooperation among filmmakers. Here's a look back at ten notable titles: 13 Assassins (Miike Takashi, Japan 2010)Coming off as more square - but no less wildly entertaining - than many of his previous films, Miike Takashi's latest...
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.695 | 0.439 |
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".