Central planning, market and subsistence from a tundra perspective: Field experience with reindeer herders in the Kola Peninsula
Bibliographic record
Abstract
This paper is based on field experience in the tundra camp of a reindeer-herding brigade with mixed ethnic background (Komi, Sami, Nenets, Russians) belonging to the ex-Sovkhoz of Krasnoschelie. Its purpose is to situate the new critical issues facing the reindeer-herding collectives after the economic collapse in Russia in 1998. My main argument is that the increasing economic isolation of the tundra periphery forces the herders to redefine their relationship with both the centre(s) and the other tundra actors. Reindeer herding on the Kola Peninsula is analysed in relation to its heterogeneous economic system defined by the old Sovkhoz-like management and the new Western buyer of reindeer meat. Furthermore, the social environment in the herding territories has changed since the deterioration of the central planning economy, implying new renewable resources' users. After massive loss of jobs, militaries, miners and geologists came into the tundra for substantial hunting and fishing and so became actors in the local informal economy. Finally, tundra-located herders and hunters seem to be somewhere unified by a discourse against the town-based administrative power and economic actors such as mining industry. Therefore herders have to deal with both an old administrative system in the agrocentre and new realities in the tundra. Based on a case study of herding/hunting activities in a tundra camp, the paper analyses the social relationships between the different actors in the post-Soviet Kola tundra and express their quest for solutions.
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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 | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".