Tout le monde a sa place? MIRAB, education, and society in Wallis and Futuna
Bibliographic record
Abstract
Wallis and Futuna officially became a 'Sub-National Island Jurisdiction' (known as French Overseas Territory/Collectivity) in 1961 and has since adopted an economic and political structure consistent with the MIgration, Remittances, Aid, and Bureaucracy (MIRAB) model.Although the MIRAB economy has been stable, the rapid increase in migration for higher education of the younger population since the turn of the century raises questions concerning the role of education within this structure.A qualitative study discovered that education should be regarded as a key component in the MIRAB economy of the territory.Firstly, education has provided a new population outlet as young people have chosen opportunities for higher education and training overseas.Secondly, migration for education has revealed social dimensions within the model, as access to education overseas has simultaneously sustained the bureaucratic elite but has also enabled customary classes to negotiate their power and maintain a hold on the process of change.Finally, education has potentially reduced the pressure of remittances, as some qualified graduates are able to return and access high wage employment locally.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".