Long Term Migratory Dynamics and Context of a Territory: French Guiana
Bibliographic record
Abstract
France, like other former European immigration countries, such as Belgium, the Netherlands, Germany and the United Kingdom, has recorded significant waves of foreign workers, sometimes accompanied by members of their family. In the 1950’s and 1960’s until mid-seventies, the first oil shock put a stop to active policies of recruiting foreign workers, but until today, immigration has not ceased fueled mainly by family reunification and the influx of refugees. From this context, it is important to ask the following question: how have migratory policies evolved in France since 1901 till date? How is this development beneficial to the regional economic integration of Guyane-one of the French territories bordered by Brazil and Suriname? The primary objective of this article is to compare migratory policies from 1901 to present day and to examine their impact on the integration and economic development of Guiana as well as their causes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".