Bibliographic record
Abstract
EnglishAn increasing number of islands can be identified -over a hundred thousand islands, most of them of small size: islands along the coast shredded by the big glaciers (Norway, Canada, Alaska, Chile), the atolls of the Pacific or those located along the big volcanic, chains from the Aleutians to the West of Sumatra. It easy to classify these islands according to their sizes: those which measure only a few tens of metres or a few hundred metres. The clustering of islands forming archipelagos reflects the interplay of geopolitical forces which has been going on for a more or less long period of time. Islands today also do have geopolitical interactions with the countries located on more or less close continents. Different case studies are examined in this article. francaisOn peut recenser un nombre d'iles de plus en plus gran -plus d'une centaine de milliers- de tres petite taille pour la plupart: le long des cotes dechiquetees par les grands glaciers (Norvege, Canada, Alaska, Chili), atolls du Pacifique ou sur les grandes cordilleres volcaniques qui vont des Aleoutiennes a l'ouest de Sumatra. Il est commode de classer ces iles par ordre de grandeur: celles qui ne mesurent que quelques dizaines ou centaines de metres, celles qui ne mesurent en kilometres, en dizaines de km, en centaines de km. Le regroupement d'iles en archipels traduit divers jeux de forces geopolitiques plus ou moins anciennes. Geopolitiques sont les rapports actuels que les iles ont avec des Etats situes sur des continents plus ou moins proches. Differents cas sont evoques dans cet article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".