Bibliographic record
Abstract
HIGHLIGHTS FROM THE TORONTO INTERNATIONAL FILM FESTIVAL 2014 Although Toronto raised the stakes in the bloodsport of festival politics by saving its prime opening weekend slots only for world premieres, it's still early to tell if it can be kingmaker of the US awards season. Ironically, interest in the opening weekend haul of its 39th edition (4 - 14 September 2014) was muted, while films tipped as derby favourites had premiered elsewhere. In this game of give-and-take, a win for Toronto's ego can mean less exposure to important films. Away from Hollywood buzz, several directors returned with new works for the second year running: Peter Chan, Lav Diaz, Hong Sang-soo, Sono Sion, Johnnie To and Tsai Ming-liang (for Hong and Sono, it's their third year running). And for its sixth 'City to City' program, Toronto chose Seoul with eight titles spanning the genre variety that has sustained South Korean...
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.529 | 0.214 |
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".