Is Siberian Reindeer Herding in Crisis? Living with Reindeer Fifteen Years after the End of State Socialism
Bibliographic record
Abstract
Abstract Most commentators on Siberian reindeer herding conclude that the dramatic drop in the numbers of domestic reindeer after the collapse of state socialism point to a crisis in reindeer husbandry. This article argues that instead of focusing on numbers we should focus on the way people form new relationships with reindeer in order to take advantage of opportunities thrown up by the post-Soviet landscape. By making reference to two case studies in the taiga and treeline areas, the author gives examples of 'interstitial practices' that reindeer herders use to survive and profit from fractured and over-regulated spaces. The author argues that the unique skills of reindeer herding, which allow people to alter the way that they use space and react to temporal pressures, give post-Soviet reindeer herders a unique adaptive strategy in a post-Soviet economy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".