Thunder Bay: Local news is important for conversations on reconciliation
Bibliographic record
Abstract
[Paragraphs 1 to 3] The Ontario city of Thunder Bay is in the headlines these days for all the wrong reasons. Canada’s highest rates of murder and violent crime. The highest number of hate crimes per capita. Systemic racism embedded in shoddy police investigations. The deaths — many unexplained — of Indigenous students who come to the city for education not available in their remote northern communities. For years these troubles and the inequitable relationship between the Indigenous and non-Indigenous populations in the city festered. Then in the spring of 2011, the Toronto Star began publishing reporter Tanya Talaga’s stories about the deaths of seven young Indigenous students over the previous decade. What had been a local story vaulted into national headlines. Talaga’s reporting became the basis for her 2017 award-winning book Seven Fallen Feathers: Racism, Death and Hard Truths in a Northern City.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.225 | 0.066 |
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".