Bibliographic record
Abstract
J’ai été initié à la Floride dès l’âge de six ans, alors que mes parents séjournaient annuellement quelques semaines par année dans les motels fréquentés par les Québécois dans les banlieues de Miami, telles Surfside et plus tard Hollywood-Beach. Ces séjours m’ont emmené à faire un doctorat à l’Université d’Ottawa sur les dimensions sociospatiales qui sont à la base de la communauté de Floribec (Hollywood-Dania-Hallandale, Floride). Toutefois, je réalisais rapidement que d’étudier un terrain continuellement victime de railleries de la part des médias, de certaines couches socioéconomiques de la société québécoise et, aussi, de collègues s’avérerait un défi. J’ai accepté ce défi sans hésiter. Aujourd’hui, Floribec demeure encore une destination touristique et un terrain de recherche qui me passionne profondément, mais je dois continuellement défendre non seulement le caractère « savant » de ce foyer francophone en terre étatsunienne, mais aussi ceux qui le fréquentent comme touristes et ceux qui y ont immigré. Ce cours récit personnel relate brièvement ma passion pour la Floride québécoise comme touriste, comme étudiant et comme chercheur.
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 | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.013 |
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".