Laws of domestication and domesticating the law in Yakutian human–animal relations
Bibliographic record
Abstract
Any contemporary practice of humans with animals is regulated by authorities. Within the Arctic, the Republic of Sakha (Yakutia) has been particularly active in legally protecting breeds of animals that the authorities have decided to be important in the area. Laws on reindeer herding, horse herding, and native cattle establish the area as an exceptional place of multi-species pastoralism in the Arctic. In this chapter we analyze the laws according to their orientation toward economic, social–cultural, and genetic significance of the respective animals. Significance, however, is defined by human interests. Legal anthropological analysis shows to what extent specific laws for specific breeds of animals influence livelihoods and cultures of people. We argue for a closer investigation of the overlap between regulations on paper and realities created by lived human–animal experience. In doing so, this chapter draws parallels between people’s efforts in enacting the domestication in their human–animal relations, as well as in their relations to the law. Thus, laws become meaningful for human–animal livelihoods through domesticating them locally: in a mutual process laws are made that fit within the local pastoral livelihood, as well as pastoral practices being adjusted to benefit from laws.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".