Dominant Crisis Narratives and Changing Infrastructures
Bibliographic record
Abstract
Abstract In Chapter 4, we examine efforts to address reckoning at one of Canada’s most respected legacy journalism organizations: the Toronto Star. Methodologically, we draw on a number of sets of data: public and policy discourse about the journalism crisis in Canada, recent events related to race and gender at the Star, and ethnographic fieldwork and interviews with Star journalists regarding the development of data journalism. Our analysis generates questions about how news organizations are wrestling concurrently with structural critique, economic challenges, and technological transformation. The gender, race, and colonial reckoning that we find in other chapters, we see internally at the Star where long-standing issues with “the view from nowhere,” the challenge of closed systems of journalism, and legacy organizations’ openness to change are conjoined with issues such as methodological interpretation, journalism’s colonial history and its systematic whiteness, and exclusion of Indigenous and minority journalists.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".