Nature's Past Episode 012: Industrialization in Subarctic Environments
Bibliographic record
Abstract
Between 1920 and 1960, Canada’s northwest subarctic region experienced late-stage rapid industrialization along its large lakes. These included Lake Winnipeg, Lake Athabasca, Great Slave Lake, and Great Bear Lake. Powered by high-energy fossil fuels, the natural resources of the northwest were integrated into international commodity markets and distributed throughout the world. Whitefish from the large lakes found their way onto dinner plates in New York while uranium from Canada’s northwest fueled the world’s most destructive weapons, atomic bombs. \n \nProfessor Liza Piper joins us this month to discuss her new book The Industrial Transformation of Subarctic Canada from UBC Press. This book explores a region unfamiliar to most Canadians and how that space was transformed through industrial processes in the twentieth century. Rather than finding industrial technologies dominating the landscape of the northwest, Professor Piper found that humans used those technologies to assimilate nature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".