Compte rendu du colloque "Méthodes Quantitatives en Sciences Humaines (MQSH) 2023"
Bibliographic record
Abstract
Le 9 juin 2023 s’est tenu le 12e colloque annuel Méthodes quantitatives en sciences humaines à l’Université TÉLUQ, Montréal. Sept conférenciers ont présenté leurs résultats de recherche. Sébastien Béland a évalué l'efficacité de différents coefficients populaires sur des items unidimensionnels à réponse dichotomique. Félix Laliberté a présenté StepMix, un package Python et R pour le clustering basé sur des modèles de mélanges généralisés (classes latentes, profils latents). Eric Frenette a fait part des défis méthodologiques et statistiques rencontrés lors de l'évaluation du programme longitudinal \emph {Gagnant pour la vie} (version 1.0) implanté dans une école à concentration sport au Québec. André Achim a présenté une nouvelle technique de symétrisation de distributions asymétriques. Pier-Olivier Caron a présenté une comparaison des propriétés statistiques (puissance, erreur de type I, biais) des modèles de médiations longitudinaux et transversaux. Denis Cousineau a présenté deux techniques analogues à l'ANOVA : l'analyse des proportions utilisant la transformation arcsine (ANOPA) et l'analyse des fréquences de données (ANOFA). Louis Laurencelle propose un test complet et vraisemblable sur le plan paramétrique de la différence entre deux proportions jumelées. \\ The 12th annual meeting Quantitative Methods in the Humanities (Méthodes quantitatives en sciences humaines, MQSH) was held on June 9, 2023 at Université TÉLUQ, Montreal. Seven speakers presented their research findings. Sébastien Béland assessed the effectiveness of various popular coefficients on unidimensional items with dichotomous responses. Félix Laliberté presented StepMix, a Python and R package for model-based clustering and mixture (latent classes, latent profiles). Eric Frenette presented the methodological and statistical challenges encountered during the evaluation of the longitudinal program \emph {Gagnant pour la vie} (version 1.0 ; Eng. Win for life) implemented in a sports-oriented school in Quebec. André Achim presented a new technique for symmetrizing asymmetrical distributions. Pier-Olivier Caron presented a comparison of the statistical properties of longitudinal and cross-sectional mediation models. Denis Cousineau presented two techniques analogous to ANOVA: analysis of proportions using the arcsine transformation (ANOPA) and analysis of data frequencies (ANOFA). Louis Laurencelle proposed a complete and parametrically plausible test of the difference between two paired proportions.
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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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.154 | 0.069 |
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".