The Pitfalls and Possibilities of Labour Movement-Based ELearning, http://home.oise.utoronto.ca/~psawchuk/casae.pdf Rather sketchy and patronising in places, but does identify some of Africa’s constraints (incl cultural) and argues for �� a learner suppor
Bibliographic record
Abstract
Abstract: This paper presents findings on informal learning and labour movement-based e-learning. It examines the participation of 40 labour activists from across Canada during a six week, online workshop, and focuses on the relations between online and offline activity. It suggests that e-learning can be used to support the goals of collective action and solidarity, and that the medium can contribute to the development of a working-class narrative that makes meaning in and energizes the lives of labour activists. Résumé:Cet exposé a pour but de présenter les résultats dune étude sur lapprentissage informel et sur lapprentissage en-ligne dun mouvement ouvrier. Nous examinons la participation de 40 travailleurs activistes, à travers le Canada, pendant un atelier de six semaines offert en-ligne. Nous avons porté une attention particulière aux relations formelles et informelles entre lactivité en-ligne et hors-ligne. Cette étude suggère que lapprentissage en-ligne peut être utilisé en support des objectifs dune action collective et solidaire, et que le médium peut contribuer au développement dun narratif de classe ouvrière qui fait du sens et énergise la vie des travailleurs activistes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".