Review Essay: Changes in the Attribution of Values to Northern and Arctic Spaces
Bibliographic record
Abstract
Review of: Silje Gaupseth, Marie-Theres Feerhofer & Per Pippin Aspaas (eds.), Travels in the North, Hannover: Wehrhahn Verlag 2013, Troll Band 13, Tromsø Studien zur Kulturwissenschaft, ISBN 9783865253347, 230 pp.;John McCannon, A History of the Arctic. Nature, Exploration and Exploitation, London: Reaktion Books Ltd. 2012, ISBN 9781780230184, 349 pp.; Martin Breum, Når isen forsvinder. Danmark som stormagt i Arktis, Grønlands rigdomme og kampen om Nordpolen, København: Gyldendal 2013, ISBN 9788702138023, 316 pp.;Barry Scott Zellen (ed.), The Fast-Changing Arctic. Rethinking Arctic Security for a Warmer World, Calgary: University of Calgary Press 2013, ISSN 1701-0004; co-published by the Arctic Institue of America, ISBN 97815523-6460, 395 pp.; Miyase Christensen, Annika E. Nilsson & Nina Wombs, Media and the Politics of Arctic Climate Change. When the Ice Breaks, Houndmills, Basingstoke, Hampshire UK: Palgrave Macmillan 2013, ISBN 9781137266224, 182 pp.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".