OCanada-TVNews: A Two-Year Dataset for Gender and Topic Diversity in Canadian TV News Broadcasts
Bibliographic record
Abstract
This paper presents OCanada-TVNews, a two-year dataset of Canadian TV news broadcasts, from four major channels: CBC The National, CTV The National, Global News, and RadioCanada Info, retrieved from channels’ YouTube playlists. The dataset consists of 3982 videos, in total 999 hours. Each video is processed through a systematic pipeline that involves audio extraction, speaker diarization, gender detection, and topic classification. In addition, for each video, metadata containing user engagements is also provided. By merging speaker segments with gender labels and topic categories, this dataset offers a detailed view of who speaks and what is discussed on a daily basis. An exploratory analysis highlights persistent disparities in gender representation and provides insights into the topics that dominate coverage. OCanada-TVNews serves as a basis for a further study of media diversity, content trends, and potential biases in broadcast journalism.
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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 | 0.000 |
| 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 | 0.000 |
| 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".