Bibliographic record
Abstract
Lisbeth Lewander did not live to nish work on her contribution to this volume. Nevertheless, her work was so central to the entire Arctic Nordic project, and her own insights and reections are so important for the overall results, that I decided, as project leader and editor, to try to bring something from her work to the published book. is chapter is based on her extended abstract, entitled ‘Science for Politics and Politics for Science’, which was submitted in April 2011, and a popular essay in Swedish published in the autumn of 2011, ‘Nordens arktiska pereri – fragment fran ett faltarbete’.1 e Swedish text was in turn based on extended eldwork that she carried out in preparation for her nal parts of the Arctic Nordic project, which were to deal with security-related issues involving Arctic and North Atlantic science collaboration between the Nordic countries, the United States and Canada. I have edited her texts slightly and inserted references in footnotes to assist the reader but have tried not to depart from the personal essay style that the text had in its original Swedish version and in her early eldwork report. I would like to thank Dieter Muller, Jessica Shadian and Urban Wrakberg for valuable bibliographic advice and Douglas Smith for adding key information on the history of Churchill.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".