Polar policy making: Two ethnographic accounts of Polar field scientists interacting with Polar governance and policy
Bibliographic record
Abstract
This review article discusses the recent publications Studying Arctic Fields by Richard C Powell and The Technocratic Antarctic by Jessica O’Reilly. Both books are ethnographic accounts of scientists working in the Polar Regions that analyse interactions at the science–policy interface. Studying Arctic Fields is a detailed story of Canada’s Resolute research station, based on immersive ethnographic observation and communicated through an engaging narrative of colourful stories from Powell’s two summers among the scientists and support staff there. The Technocratic Antarctic treads new ground in its examination of Antarctic social science, presenting the findings of a wide-ranging and thorough research project that engages with the themes of territory, security, processes, practice, problems and science communication. Both publications make valuable contributions to Polar social science and will also appeal to many beyond this.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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 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".