A Compass During the Storm: Offering Students Critical Rigor for Polarizing Times
Bibliographic record
Abstract
Teaching and learning in institutions of higher education is occurring, unavoidably, within the broader civic context of today’s extraordinarily polarizing political times. In Canada, public consciousness has recently been buffeted by a contentious 2019 federal election process; taken in the further light of the US’s 2020 presidential campaign, and global tumult spanning the horizon immediately beyond, we seek to help our students situate themselves with respect to, and assess these points of profound contention, without ourselves contributing to exacerbated polarization. We aim to offer students in our first-year exploratory political science course a vital tool—critical rigor—for navigating, but not being inundated by the storm. This paper discusses our experiences in teaching our course, “The Worlds of Politics”, as we have attempted to help students meaningfully engage in cognitive processes of critical analytic thinking, without undue infringement from their own, and least of all our, personal political biases.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".