Bibliographic record
Abstract
THE TROUBLES which wracked New Caledonia in the late 1980s, the controversy about French nuclear testing on the atoll of Mururoa in the Pacific, the periodic launching of satellites from French Guiana (Guyane) and even the ‘cod war’ between France and Canada over fishing rights near Saint-Pierre and Miquelon have intermittently brought the existence of the French départements et territoires d'outre-mer (DOM-TOMs), France's overseas outposts, into a wider focus. The ten DOM-TOMs are strategically scattered around the world in the Atlantic, Caribbean, Pacific and Indian Oceans and in Antarctica. Despite their distance from France, the metropolitan ‘hexagon’, the départements d'outre-mer (DOMs), are legally as much a part of France as Paris or Marseille. The DOMs, at least in theory, have institutions and legal systems that replicate those of the métropole ; the territoires d'outre-mer (TOMs), although enjoying greater autonomy and particularistic institutions, are also legally part of the French Republic. Many of the DOM-TOMs have been part of France much longer than Nice and Corsica. The remnants of France's once vast overseas empires, they account for a population of one and a half million French citizens and cover a land area of over 120,000 square kilometres, even excluding the French region of Antarctica. With the recognition of exclusive economic zones in the Law of the Sea agreements, the DOM-TOMs give France the third-largest maritime area in the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.210 | 0.045 |
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".