Bibliographic record
Abstract
In 2021, the world is dominated by two intense discussions: environmental sustainability and the COVID-19 pandemic.It is tting, therefore, that this issue of the Northern Review provides a series of perspectives on these topics, ranging from Indigenous engagements with Greenpeace, to the role of the Canadian Rangers in disaster mitigation, to mining pollution and wastewater management.While this issue is not exclusively focused on these themes-papers on aff ordable northern housing and the Arctic Council continue to broaden the reach of the journal-questions about how the North can create a pro table and successful economy while protecting the environment, and how this vast and thinly populated area can respond to crises, remain front and centre.For much of the global pandemic, the North has been one of the leading regions in battling the spread of COVID-19.Scandinavia has generally done quite well.Remote communities in Alaska and the Canadian North stopped traffi c into their settlements in a calculated attempt to hold back the latest biological threat to their viability.And they did so well.In the early summer of 2021, however, things shifted.e Yukon, with one of the highest vaccination rates in the world and an enviable track record for protecting its citizens, suddenly was hit by the highest infection rates in well over a year.Nunavut, which had locked itself down tightly, with strict quarantine requirements and tight controls on movements into the territory, faced a distressing series of outbreaks.e pandemic experiences speak to a central theme in northern life: vulnerability.Northerners are always subject to the climatic realities of living in the Arctic and Subarctic, including extreme cold, blizzards, river ooding, and the like.ey rely on tenuous lifelines to southern suppliers, previously reliant on seasonal shipping, now supplemented by air travel and, perhaps, with airships in the future.But the North is vulnerable, too, to mining disasters or poorly planned mitigation
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.343 | 0.250 |
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".