Using identity politics to address artworld issues : a case study of the New Initiatives in Film program at the National Film Board of Canada
Bibliographic record
Abstract
The Canadian government introduced its Multicultural and Employment Equity policies in a series of attempts to induce federally-controlled institutions to reflect the racial diversity of the Canadian population in their programs and workforces. This is a case study of one institution's response to these policies. It examines the implementation of the six-year New Initiatives in Film (NIF) program begun in 1990 by the now-defunct women's filmmaking unit, Studio D of the National Film Board of Canada (NFB) and exposes the fault lines along which the goals of the NFB's various constituent parts clashed and meshed with the diverse goals of various parties in NIF's target communities (i.e. "emergent aboriginal and 'of colour' women filmmakers"). I argue that because the NIF program was structured according to the politics of identity ("race" in this case), "artworld" issues of unfair hiring and funding practices in the Canadian film industry, became distorted and expressed as issues of identity. Obfuscating the professional dynamics in the world of Canadian filmmaking by using "race" as an organizing principle did not, in the long-term, assure the sustained inclusion of excluded groups within mainstream institutions. A more effective strategy, the data suggests, would have been for underrepresented groups to cultivate alliances with professionals in the filmmaking industry based on concrete occupational, rather than hypothetical race-based interests.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.057 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".