Bibliographic record
Abstract
FRANCE'S TEN overseas departments and territories are the remains of more than four centuries of imperial expansion and contraction which, at various times, gave France sovereignty over large areas of North America, Africa and Asia, as well as, more briefly, much of the European continent and the East Indies. Since at least the time of the Crusades, French voyagers have moved outwards to explore, trade, proselytise, settle and sometimes to conquer. In the 1500s and 1600s France put together its first empire, centred in the Antilles but including parts of eastern Canada and the Mississippi basin and outposts in Africa and India. Most of this empire was lost to England in the 1700s. Then, from the early 1800s, after the defeat of Napoleon's ambitions for a Levantine or a Continental empire, France created a second overseas empire embracing islands in the Pacific and vast domains in Africa and Asia. At its height in the 1930s, this French empire counted some 12 million square kilometres of territory and almost 68 million subjects alongside 1.5 million French settlers. Later in the twentieth century, France relinquished almost all its vast second empire through decolonisation, sometimes easily, sometimes very painfully. The ten DOM-TOMs are the legacy that history has left to France far beyond its European shores.
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 | 0.000 | 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 | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".