Bibliographic record
Abstract
We all fail. We also like to look good and avoid looking bad. So, even though we know that taking risks and trying new approaches are important for enhancing our teaching and students’ learning (Strean, 2017), we rarely talk about our failures. Our claim in this paper is that our insecurities create a substantial barrier to improving and enriching our teaching practices. If we do not find time to take big risks, and then to explore and critically reflect on failures that result sometimes from those risks, we lose out on the chance to become better teachers; more fundamentally, we deprive our students of the chance to have extraordinary opportunities to learn. Nous connaissons tous des échecs. Or, nous voulons projeter une image positive de nous-mêmes. Ainsi, même si nous savons qu’il est important de prendre des risques et d’essayer de nouvelles approches pour améliorer notre enseignement ainsi que l’apprentissage de nos étudiants (Strean, 2017), il est rare que nous parlions de nos échecs. Dans cet article, nous avançons l’idée suivante : notre manque d’assurance constitue un obstacle considérable à l’amélioration et à l’enrichissement de nos pratiques d’enseignement. Si nous ne nous donnons pas du temps pour prendre des risques importants, puis pour réfléchir de manière critique sur les échecs qui découlent parfois de cette prise de risque, nous laissons passer une occasion de nous améliorer en tant qu’enseignants. Qui plus est, nous privons ainsi nos étudiants d’occasions d’apprentissage exceptionnelles.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.419 | 0.219 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".