Mind The Gap Exploring The Experiences Of Diasporic Media Producers And Representations Of Cultural Diversity In Canada
Bibliographic record
Abstract
Prime-time narrative is the most watched and influential genre of television. It creates a sense of belonging and contributes to identity formation. It also receives the largest amount of publicly mandated funding in the form of investment, subsidies, and tax incentives in Canada. Anecdotal and empirical evidence suggests that despite legislation requiring equitable representation in all aspects of screen media, and over thirty years of many “special initiatives” and training and mentorship programs, little progress has been made in the area of equitable representation in narrative programming. This dissertation investigates the representation of diasporic people of colour in the screen-media industry in Canada. In particular, it studies how “authentic voices” from these communities are finding expression in the area of prime-time television narrative programming (scripted comedy and drama) and feature films, which ultimately find their largest audiences in broadcast screen platforms on television and increasingly via the Internet. The focus is on the legislative frameworks pertaining to the reflection of “diasporic communities of colour” in the production of screen media, specifically for prime-time broadcast in narrative, or what is referred to in the industry as “scripted programming,” as well as on the current realities faced by creators of screen media from diasporic communities of colour in telling their stories in this arena. Through a case study of the television series Little Mosque on the Prairie, it examines the issues that affect the expression of “authentic voice” from individuals who have had the opportunity to work in the area of narrative screen media in the Canadian Broadcasting Corporation, Canada’s public broadcaster, which has as one of its key priorities the reflection of the cultural diversity of Canada. The issues involved in the production of feature films by diasporic people of colour is examined through a case study of the film Heaven on Earth, written and directed by Indo-Canadian filmmaker Deepa Mehta. Mehta’s film presents a unique situation in which the filmmaker, due to the previous international success of her film Water, was able to access the financial resources to produce the film in Canada and maintain her “authentic voice” without mediation in the production from external players. Part of this case study includes a documentary film featuring an interview with Deepa Mehta conducted in 2017 about her film Heaven on Earth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".