Bibliographic record
Abstract
Aim/Purpose: The purpose of this research is to identify within the arts and culture sphere and, more specifically, the film industry, what kinds of employment opportunities are afforded (or not) to BIPOC communities, specifically Black communities in Quebec? How are Black communities in Quebec represented in the local film industry, both in front of and behind the camera? In what ways are Black stories being told, how are they being represented, and how many Black people are actually telling their own stories across media? Background: This paper attempts to lay out the general state of the film industry within Canada, focusing on Quebec’s Black communities. Methodology: Using an intersectional approach, I draw from a wide range of ages, backgrounds, languages, and experiences that will cover the range of roles affected at each level of the industry through in-depth interviews. This will be accompanied by a self-reflexive comparison to my experiences navigating the film industry during university and after within the labor market. Findings: The results of this research demonstrate that there is a distinct divide between how Black communities see themselves represented in front of and behind the screen within different parts of the film industry. Impact on Society: Due to exclusionary practices and lack of investment in BIPOC storytelling, the ways in which BIPOC creatives and specifically those in the Black community have to find ways to navigate outside mainstream film industry circuits to create.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.024 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".