F.Stammler, Arctic climate change discourse: the contrasting politics of research agendas
Bibliographic record
Abstract
In this paper we explore howWestern scientific concepts and attitudes towards indigenous knowledge, as they pertain to resource management and climate change, differ from the prevailing view in modern Russia. Western indigenous leaders representing the Inuit and Saami peoples are actively engaged in the academic and political discourse surrounding climate change, whereas their Russian colleagues tend to focus more on legislation and self-determination, as a post-Soviet legacy. We contribute to the debate with data from the Nenets tundra, showing how different research has employed the three crucial Western research paradigms of climate change, wildlife management and indigenous knowledge on the ground. We suggest that the daily practice of tundra nomadism involves permanent processes of negotiating one’s position in a changing environment, which is why “adaptation ” is woven into the society, and cosmology as a whole, rather than being separable into distinct “bodies ” of knowledge or Western-designed categories. We argue that research agendas should be placed in their proper local and regional context, and temporal framework: for example, by collaborating with herders on the topics of weather instead of climate change, herding skills instead of wildlife man-agement, and ways of engaging with the tundra instead of traditional ecological knowledge.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".