Creating space for authentic voice in Canada's screen industry: A case study of 'Women In the Director's Chair (WIDC)'
Bibliographic record
Abstract
Using an appreciative inquiry approach and sharing a reflexive 4-D (i.e., discovery, dreams, design, delivery / destiny) narrative that explores societal, organizational and personal perspectives, this action research study describes a specially designed, internationally respected Canadian national professional development initiative for women screen directors, entitled ‘Women In the Director’s Chair (WIDC)’. The narrative traces how this initiative came to be, and within the context of North America’s ‘waves’ of feminism, where it is placed on the landscape of Canada’s screen industry. While foregrounding a well-documented socio-cultural ‘lack of confidence’ in women leaders and in particular in women screen directors in Canada, the study contextualizes the personal ‘leadership experience’ narratives of WIDC director participants while the author makes meaning of her own leadership journey as a co-creator of the WIDC initiative. The author further explores the twenty-two-year evolution of WIDC’s transformation-oriented pedagogical design as she reflects on the positive core of WIDC and asks, ‘What is WIDC? What was learned and what’s next?’ Sharing leadership metaphors that offer guidance for navigating a ‘continuum of confidence’ and offering a theoretical map towards transformation for individual women as well as feminist or like-minded organizations, the study concludes with a call to action to adopt an appreciative growth-minded stance in order to create space for authentic voices to thrive in Canada’s screen industry, in particular the voices of female leaders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.054 | 0.025 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".