Hearing from and Listening to: Dialectical Tensions in the Pedagogical Pursuit of Critical Analysis
Bibliographic record
Abstract
As instructors, it is not uncommon to find ourselves faced by a lack of enthusiasm from students when we ask them to participate in the critical analysis of complex issues. It can be difficult to know if it is an outcome of their having limited knowledge of the subject, fear of their perspectives being negatively judged by peers and instructors or an uncertainty of how to think critically (not just negatively) about an issue. An outcome of any of these factors can be a silent class or one dominated by a few voices, leaving the instructor unsure as to whether the time has been well spent. This session built on the lessons learned from our experiences of (more or less) engaging students in the critical analysis of racialized discourses. During the presentation, we identified strategies for teaching students how to critically analyze materials, and shared our classroom experiences of engaging students in these processes. Those attending the presentation were invited to describe their own instructional experiences and to elaborate on processes they found helpful or unhelpful. There was opportunity for participants to utilize the strategies presented through analysis of racialized materials.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".