L'interculturel en temps de pandémie
Bibliographic record
Abstract
Avant-propos : Le réflexe interculturel / Bob W. White ; Représentations médiatiques interculturelles du COVID-19 / Joseph Lévy ; Stigmatisation et contagion pandémique / Pierre Anctil ; « Distanciation sociale » : vraiment? / François Rocher et Bob White ; Précarité du travail et inégalités sociales de santé : quelles leçons à tirer de la pandémie de COVID-19 / Daniel Côté, Jessica Dubé et Jorge Frozzini ; La mort et les rites mortuaires en temps de pandémies / Joseph Lévy et Maude Arsenault ; Plateformes numériques en temps de crise : quels usages des médias sociaux pendant la crise sanitaire de la COVID-19? / Farrah Bérubé ; Vulnérabilité et résilience sociales en contexte de pandémie / Isabelle Comtois et Bob W. White ; L’état de soins aux aînés au Québec : un appel pour une autopsie des CHSLD au-delà de la COVID-19 / Kalpana Das.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".