Bibliographic record
Abstract
FQ Columnist Bilal Qureshi reflects on Deepa Mehta's film Earth at an important moment in Indian and global history. Writing from New Delhi, he had the opportunity to speak to Mehta in person about her life and work, and that discussion is woven into this column. Since making Earth almost twenty years ago, Deepa Mehta has seen her stature grow to include film festival premieres, an Oscar nomination, and a platform as one of the rare women auteurs on the international stage. She has lived in Canada since the 1970s, but her most celebrated films are not about immigrant displacement or hyphenated identity. Rather, she has always told Indian stories. From the groundbreaking story of a lesbian relationship between two housewives in suffocating arranged marriages (Fire, 1996) to the forced exile of widows in orthodox Hindu scripture (Water, 2005), she has confronted uncomfortable social realities in Indian society. Although she has been labeled an anti-national and had sets burned and cinemas attacked by the religious right for insulting traditional values, she has taken the challenges in stride and continued making films.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.771 | 0.561 |
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".