How Did the Media Report the Mining Industry’s Initial Response to COVID-19 in Inuit Nunangat? A Newspaper Review
Bibliographic record
Abstract
Mining in Inuit Nunangat relies on a southern Canada fly-in/fly-out (FIFO) and local workforce. The FIFO workforce, combined with existing social determinants of health, can create health risks to Inuit Nunangat. These risks were increased with COVID-19. As newspaper reporting can shape public opinion and policy actions regarding these COVID-19 risks, we systematically searched databases to identify newspaper articles during the initial phase of COVID-19 (i.e., articles published from 1 January to 30 June 2020). Descriptive statistics and qualitative thematic analysis were used to analyze the nature, range, and extent of included articles. Most included articles were published by Inuit Nunangat-based newspapers. Half the sources quoted were mining companies and most reported reactions to their initial response were negative. The most frequent topic was concern that an infected FIFO employee could transmit COVID-19 to a worksite and subsequently infect Inuit employees and communities. Inuit Nunangat-based newspapers were crucial in shaping the narrative of the initial response. National newspapers mainly focused on the takeover of TMAC™ during the pandemic, while Inuit Nunangat-based newspapers provided timely and locally-relevant pandemic information. Without Inuit Nunangat-based newspapers, the reporting would be from national and southern newspapers, which was less in-depth, less frequent, and less relevant to Inuit.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".