Bibliographic record
Abstract
The introduction of a new ‘Cities’ sidebar focusing on Tel Aviv provoked a fierce, divisive and highly public controversy at the 2009 Toronto International Film Festival (TIFF). Sponsored by the Israeli government's Brand Israel campaign, the sidebar featured ten films set in Israel's administrative capital and, while none of the films was, in itself, especially controversial, the premiss – that a major international film festival would accept money directly from an Israeli government eager to promote an alternative media image to the one associated with their nation's long and controversial involvement in Palestine – elicited an angry response from a number of public figures. Endorsing what became known as the ‘Toronto Declaration’, the signatories, including Frederic Jameson, Naomi Klein, Ken Loach and Slavoj Žižek, argued that the celebration of Tel Aviv was inappropriate given Israel's widely condemned actions in the Occupied Territories, particularly the invasion of Gaza in December 2008. The Declaration stated: As members of the Canadian and international film, culture and media arts communities, we are deeply disturbed by the Toronto International Film Festival's decision to host a celebratory spotlight on Tel Aviv. We protest that TIFF, whether intentionally or not, has become complicit in the Israeli propaganda machine.1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".