Bibliographic record
Abstract
This article addresses an issue arising from a comparative study of the nature of education and training for Canadian full-time union staff and officials. The specific question is how can education for union officials address both the social and the servicing demands placed on them? The article locates the discussion about such training within the contexts of existing approaches to labour education and current debates about the revitalization of the labour movement. It concludes with a call for more systematic discussion of these issues and further analysis of different programmatic training models. Cet article porte sur une question découlant d’une étude comparative de la nature de l’éducation et de la formation données aux dirigeants et dirigeantes et membres du personnel à plein temps des syndicats au Canada. La question est celle de savoir comment l’on peut voir à ce que la formation donnée à ces « cadres » syndicaux leur permette de répondre aux exigences sociales et de prestation de services qui leur sont imposées. L’article place cette formation dans le contexte des approches actuelles d’éducation syndicale et du débat au sujet de la revitalisation du mouvement syndical. Il se termine par un appel à un examen plus méthodique de ces questions et à une analyse plus poussée de différents modèles de programmes de formation.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".