Contemporary Challenges Teaching Labour History
Bibliographic record
Abstract
thinking about teaching as a way to help people develop their own capacities for understanding and for action.It starts not with content and assessment but with the people we are teaching.It pays less attention to facts and ideas, although clearly these remain important, and more to the dynamics of the people in the class, so we may learn from each other and teach each other.It is designed to help us examine new ideas through our experiences and the experiences of others so we may become more effective.The role of the instructor is less to deliver and test for content and more to create a place where people participate and gain confidence in their own power so they push and probe one another and themselves.Teaching in this way is not new.It is a staple of labour education where unions want to have active, militant members.It is sometimes called the "organizing model" of teaching, for it draws upon lessons from effective organizers.It starts not with the mastery of the expert but with the knowledge that people have ideas and experiences they will draw upon and that will shape how they handle new ideas.That in turn pushes the instructor to take seriously not just the content but the people in the group and to work as part of the group.It assumes that how material is delivered matters at least as much as the material itself, for the point is to help people learn how to build oppositional cultures and structures.That is hard to do with a Scantron exam and predetermined learning outcomes.The four essays that follow share some of our experiences and experiments in teaching in a more democratic and participatory way.We are not experts, but we are long-term instructors, educators, participants, and scholars of labour history and labour studies who think about, research, collaborate, and practise teaching against the grain of neoliberalism.Our hope is that readers can adopt, adapt, reject, reshape, and build on our teaching experiences.In this way, we can share and shape our educational vision and our daily practice for a democratic, participatory future.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 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".