Bibliographic record
Abstract
This essay examines the definitions of the key words of the scholarship of teaching and learning (SoTL)—scholarship, teaching, and learning—in order to identify the hopes that animate SoTL research and examine these hopes in light of recent critical thinking about the corporatization of higher education. Arguing that Biesta’s (2013b) distinction between “learning from” and “being taught by” offers an important corrective to the prevailing definitions of SoTL, the essay reflects on the tensions between scholarly teaching, as understood by SoTL, and teaching as a contingent and unpredictable event. Cet essai examine la définition des mots-clés de l’avancement des connaissances en enseignement et en apprentissage (ACEA) – avancement des connaissances, enseignement, apprentissage – afin d’identifier les espoirs qui inspirent la recherche en ACEA et d’examiner ces espoirs à la lumière des pensées critiques récentes qui portent sur la tendance de l’enseignement supérieur à fonctionner comme une entreprise. Cet essai présente l’argument selon lequel la distinction faite par Biesta (2013b) entre « apprendre de quelqu’un » et « être enseigné par quelqu’un » constitue une correction importante aux définitions actuelles de l’ACEA. L’essai propose une réflexion sur les tensions qui existent entre l’enseignement intellectuel, tel que compris par l’ACEA, et l’enseignement en tant qu’événement contingent et imprévisible.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".