L’émergence des géographies étudiantes : une littérature anglophone substantielle, une recherche francophone à bâtir
Bibliographic record
Abstract
En moins de deux décennies, la littérature anglophone portant sur les « student geographies », ou géographies étudiantes, s’est considérablement développée. Celle-ci a permis de mettre en avant des thématiques contemporaines, notamment liées aux mobilités, à la déclinaison des créations de nouvelles cultures estudiantines ainsi qu’au processus de studentification, désignant les transformations urbaines engendrées par la présence accrue d’étudiants dans des espaces résidentiels. Bien que cette littérature géographique s’intéresse principalement au contexte britannique, cet article suggère que ce champ de recherche se diffuse progressivement à l’échelle mondiale. En revanche, les publications en français, ou sur les pays francophones, ayant comme objet d’étude les populations étudiantes sont rares et sporadiques. Il apparaît donc opportun de proposer les premières pistes de réflexion quant au développement d’une littérature francophone des géographies étudiantes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".