Bibliographic record
Abstract
Toronto is a changing city that has been a source of reflection and inspiration to writers and artists whose work focuses on the conditions and prospects of human life. A city on the move, it demands policies and regulation, and it offers the pleasures and perils of the massive and the anonymous. As a site of study, the city is inherently multidisciplinary, with natural ties to history, geography, sociology, architecture, art history, literature, and many other fields. World Film Locations: Toronto explores and reveals the relationship between the city and cinema using a predominately visual approach. The juxtaposition of the images used in combination with insightful essays helps to demonstrate the role that the city has played in a number of hit films, including Cinderella Man, American Psycho, and X-Men and encourages the reader to frame an understanding of Toronto and the world around us. The contributors trace Toronto's emergence as an international city and demonstrate the narrative interests that it has continued to inspire among filmmakers, both Canadian and international. With support from experts in Canadian studies, the book's selection of films successfully shows the many facets of Toronto and also provides insider's access to a number of sites that are often left out of scholarship on Toronto in films, such as the Toronto International Film Festival. The 2014 release of this attractive volume will be a particularly welcome addition to the international celebrations of the city's 180th anniversary.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.100 | 0.007 |
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".