Cultivating Arctic landscapes : knowing and managing animals in the circumpolar North
Bibliographic record
Abstract
1. Reindeer, Caribou, and 'Fairy Stories' of State Power, D. G. Anderson 2. Uses And Abuses Of 'Traditional Knowledge': Perspectives From The Yukon Territory, J. Cruikshank 3. Local Knowledge in Greenland: Arctic Perspectives and Contextual Differences, F. Sejersen 4. Codifying Knowledge about Caribou: The History of Inuit Qaujimajatuqangit in the Kitikmeot Region of Nunavut, Canada, N. Thorpe 5. A Story about a Muskox: Some Implications of Tetlit Gwich'in Human-Animal Relationships, R. P.Wishart 6. 'We did not want the muskox to increase': Inuvialuit Knowledge about Muskox and Caribou Populations on Banks Island, Canada, M. Nagy 7. Political Ecology in Swedish Saamiland, H. Beach 8. Saami Pastoral Society in Northern Norway: the National Integration of an Indigenous Management System, I. Bjorklund 9. Chukotkan Reindeer Husbandry in the Twentieth Century: In the Image of the Soviet Economy, P. A. Gray 10. A Genealogy of the Concept of 'Wanton Slaughter' in Canadian Wildlife Biology, C. Campbell 11. Caribou Crisis or Administrative Crisis? Wildlife and Aboriginal Policies on the Barren Grounds of Canada, 1947-60, P. J. Usher 12. Epilogue: Cultivating Arctic Landscapes, M. Nuttall
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".