Promoting critically informed learning and knowing about occupation through conference engagements
Bibliographic record
Abstract
As occupation-focused discussions and applications of critical theoretical perspectives increase, attention must also be paid to how different spaces of knowledge dissemination, exchange, and production support critically informed learning and knowing about occupation. This paper presents the reflections of a group of international scholars and lecturers whose shared interest in critical theoretical perspectives prompted the incremental co-development of a series of conference engagements. We describe how our group came together, what kinds of learning experiences we developed to promote and support engagement with critical theoretical perspectives, and what understandings we gained through ongoing critical reflexivity about those learning experiences. Our discussion addresses two problematics related to conferences as learning spaces: inclusion, and sustained engagement with epistemic communities and ideas that may form through critically oriented conference sessions. We also discuss how enacting critical pedagogies and principles of ‘unconferencing’ may better promote critically informed ways of learning and knowing occupation than typical conference structures. The paper ends with a call for continued integration of varied critically informed teaching and learning opportunities at conferences, as a means of further encouraging diverse types of knowledge production, sharing, and learning about occupation.
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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 | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".