The Frame of Invisibility: A Study of Toronto Television News and \nVisible Racialized Minority Group Representation
Bibliographic record
Abstract
The mainstream media are able to potentially influence attitudes and opinions in any society. This influence may extend to how individuals view other groups and people, including members of visible minority groups. This thesis examines whether or not Practices of Exclusion and Practices of Inclusion can be observed though Toronto television news, one of the largest television news markets. This research was conducted through a simple study of observing newscasts in the Spring of 2015 and a brief update in 2018 to see if there were any changes. The research illustrates how members of visible racialized minority communities are represented in local Canadian television newscasts both in being featured as central characters in news stories and presenting the news to the audience. It takes as its object of study the Toronto television news market and has a goal of bringing about further awareness about the possible imbalance of minority representation in Toronto news media, especially in a nation that prides itself on multicultural inclusion. \nThe purpose of this thesis is to examine Toronto news media and to assess how it represents visible minorities in their news coverage. This thesis rests upon the argument that media representation of visible minorities can result in supporting an underlying system of racism because of their invisibility in news media. There is no doubt that inclusion of the various ethnic voices in Canada has improved, but the discussion is far from over.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".