Bibliographic record
Abstract
Abstract This paper is offered as part of the thirty-year celebration of the Canadian Association for the Study of Adult Education/l’Association canadienne pour l’Étude de l’Éducation des Adultes (CASAE/ACÉÉA). The paper reviews the contributions of Canadian scholars to our mapping and understanding of work and learning issues, particularly in relation to the dominant neo-liberal economic agenda. The data are essentially drawn from the proceedings of national CASAE/ACÉÉA conferences and from contributions to The Canadian Journal for the Study of Adult Education/la Revue canadienne pour l’étude de l’éducation des adultes. Résumé Cet article est proposé dans le cadre de la célébration du trentième anniversaire de l’Association canadienne pour l’étude de l’éducation des adultes/Canadian Association for the Study of Adult Education (ACÉÉA/CASAE). Le document passe en revue les contributions des chercheurs canadiens à notre cartographie et compréhension de la question « travail et apprentissage »; plus particulièrement en ce qui a trait à la domination néolibérale de l’agenda économique. Les données sont principalement tirées des comptes-rendus de conférences nationales (ACÉÉA/CASAE) et des contributions de la Revue canadienne pour l’étude de l’éducation des adultes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".