Bibliographic record
Abstract
In this chapter, we examine the perspectives of racialized Canadian journalists on how their work covers democracy and racism within the Canadian public sphere. Using semi-structured interviews, we identified six main themes using applied thematic analysis in terms of identifying, analyzing, and interpreting patterns of meaning or themes within qualitative data: (1) echo chambers in the public sphere, (2) racialized journalists&s; lived experiences, (3) the democratic façade, (4) lack of appropriate protection protocols, (5) self-advocacy and community activism, and (6) impact of systemic racism on minorities. What emerged from the discussion of these themes are ways in which racialized Canadian journalists are made hypervisible through their experiences facing harassment from the public and pressures from within the newsroom to cover stories involving racism. Simultaneously, in order to cope with these pressures and harassment, we find that journalists adopt self-effacing coping strategies which have the effect of rendering them invisible in the public sphere.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".