Julie McMullin. Understanding Social Inequality: Intersections of Class, Age, Gender, Ethnicity, and Race in Canada. Don Mills, ON: Oxford University Press, 2004.
Bibliographic record
Abstract
RÉSUMÉ Dans Understanding Social Inequality , Julie McMullin présente une discussion utile et provocatrice des différentes approches et théories utilisées pour l'analyse des inégalités sociales. Quoique l'approche de McMullin soit ancrée dans une perspective critique de l'inégalité sociale, elle présente un cadre exhaustif pour comprendre la manière dont la classe, le sexe, l'âge, l'ethnicité et la race interagissent pour produire des inégalités sociales au Canada. La discussion et l'analyse sont à la fois fines et informatives. Bien que l'auteure examine des positions théoriques complexes, elle émaille sa présentation d'illustrations concrètes, lesquelles constituent des exemples pratiques à des fins de formation. Les glossaires, questions et lectures complétant chaque chapitre faciliteront les discussions en classe. Compte tenu de la finesse des arguments de McMullin, le texte semble être destiné à des personnes familières avec les théories sociales et les méthodes sociologiques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".