“The ice has gone”: Vernacular meteorology, fisheries and human–ice relationships on Sakhalin Island
Bibliographic record
Abstract
Abstract This paper examines vernacular weather observations amongst rural people on Sakhalin, Russia’s largest island on the Pacific Coast, and their relationship to the ice. It is based on a weather diary (2000–2016) of one of the local inhabitants and fieldwork that the author conducted in the settlement of Trambaus in 2016. The diary as a community-based weather monitoring allows us to examine how people understand, perceive and deal with the weather both daily and in the long-term perspective. Research argues that amongst all natural phenomena, the ice is the most crucial for the local inhabitants as it determines human subsistence activities, navigation and relations with other environmental forces and beings. People perceive the ice as having an agency, engage in a dialogue with it, learn and adjust themselves to its drifting patterns. Over the past decade, the inability to predict the ice’s behaviour has become a major problem affecting people’s well-being in the settlement. The paper advocates further integrating vernacular weather observations and their relations with natural forces into research on climate change and local fisheries management policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".