Cultivating Arctic landscapes : knowing and managing animals in the circumpolar North
Bibliographic record
Abstract
In the last two decades, there has been an increased awareness of the traditions and issues that link aboriginal people across the circumpolar North. One of the key aspects of the lives of circumpolar peoples, be they in Scandinavia, Alaska, Russia, or Canada, is their relationship to the wild animals that support them. Although divided for most of the 20th Century by various national trading blocks, and the Cold War, aboriginal people in each region share common stories about the various capitalist and socialist states that claimed control over their lands and animals. Now, aboriginal peoples throughout the region are reclaiming their rights. This volume is the first to give a well-rounded portrait of wildlife management, aboriginal rights, and politics in the circumpolar north. The book reveals unexpected continuities between socialist and capitalist ecological styles, as well as addressing the problems facing a new era of cultural exchanges between aboriginal peoples in each region
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".