Entretien avec Stéphanie Vermeersch, Chargée de recherche CNRS 1re classe (UMR CNRS Lavue) Marie-Hélène Bacqué, Professeure de sociologie (UMR CNRS Lavue)
Bibliographic record
Abstract
Choosing the United States for analysing certain types of migration and territorial communitization of retired people is prompted by the existence of significant research carried out on migratory movements upon retirement in the United States, and also by the emergence and development of “senior citizen villages” as veritable illustrations of gated communities. These territorial “partitions” are part of a multisecular tradition of openness towards diverse types of communities which established themselves in the United States during colonization and which flourished with the various migratory waves. It is relevant here to observe these processes at work, where age emerges as pertinent criteria, not only for defining migratory flows but also for generating groups of people and for creating living spaces where, in their groups, these individuals, believing they control their aging process better, are part of a specific model of “aging well”.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".