Social Confidence in Unions: A U.S.‐Canada Comparison
Bibliographic record
Abstract
Cet article examine l’évolution des niveaux de confiance sociale envers les organisations syndicales au Canada et aux États‐Unis entre 1982 et 2006 en s'appuyant sur les données de la World Value Survey (WVS). Partant du constat que ces taux de confiance sont relativement similaires au sein de ces deux pays, nous appliquons un modèle de régression logistique sur les deux vagues de données les plus récentes du WVS (soit celle de 2000 et 2006) afin de mieux comprendre la propension des individus à faire confiance aux syndicats dans chaque contexte national en fonction de certaines de leurs caractéristiques sociales et politiques. Les résultats montrent des similitudes entre les deux pays (par exemple, les citoyens les plus progressistes et les plus jeunes ont généralement une plus grande confiance envers les syndicats), mais également des distinctions importantes (à titre d'exemple, seul au Canada les individus répondant à un profil de “classe ouvrière” manifestent une plus grande confiance envers les syndicats). This article examines changes in levels of social confidence in unions in Canada and the United States between 1982 and 2006 based on an analysis of the World Value Survey (WVS) data set. It considers why confidence rates are similar in the two countries, applying a logistic regression model to the two most recent WVS waves (i.e., 2000 and 2006) so as to bring out the effects of political and social differences on the propensity of individuals to trust unions in each national context. The results show similarities between the two countries (e.g., more progressive and younger citizens generally have greater confidence in unions), but also important distinctions (e.g., only in Canada do individuals with a working class profile appear to have greater confidence in unions).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".