Bibliographic record
Abstract
In recent years, the Canadian Broadcasting Corporation (CBC) has bolstered its diversity efforts, and in 2021 they announced a new equality, diversity, and inclusion (EDI) plan to be implemented through 2025, designed to better serve underrepresented groups in Canadian media. Throughout the past decade, CBC has made multiple efforts to increase EDI practices throughout its organization. However, scholarship has identified the limitations of previous policies in making a meaningful impact, and research documenting the underrepresentation of diverse groups in Canadian news media is almost non-existent. The objective of this study is to acquire a deeper understanding of how CBC News programming reflects Canada’s current cultural diversity by studying how diverse groups are represented in CBC News programming. To measure this, a two-week content analysis of written and televised CBC News was conducted, which explored and categorized emergent themes in the representation of diverse Canadians and the journalists who cover diverse stories. The data revealed CBC's extensive coverage of Indigenous communities and a commitment to empowering diverse journalists with the opportunity to tell such stories about the communities to which they belong. To further investigate the latter phenomenon, a discourse analysis of diverse stories written by self-identifying journalists was conducted. This section highlighted how self-identifying journalists incorporate personal experience to tell more impactful stories about the communities they identify with. Finally, this report illuminates potential oversights in CBC’s coverage of underrepresented groups in news media. This report encourages CBC to conduct an internal organizational review to evaluate how they can improve news coverage of underrepresented groups before the conclusion of their current EDI plan in 2026, suggesting the continued empowerment of self-identifying journalists and including more diverse perspectives into the newsfeed as potential solutions.
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".