Who is Represented in the Teaching Commons?: SoTL Through the Lenses of the Arts and Humanities
Bibliographic record
Abstract
As the community of Scholarship of Teaching and Learning (SoTL) scholars has flourished across Canada and around the world, there has been a growing sense among humanists that SoTL work has been dominated by the epistemologies, philosophies, and research methods of the social sciences. This is a view that has been supported by SoTL journal editors and resources dedicated to introducing faculty to SoTL. To quote Nancy Chick (2012) in a recent book on the current state of SoTL in the disciplines, “while many well-known SoTL leaders come from humanities backgrounds …, the on-the-ground work largely marginalizes the practices of their disciplines” (p. 15). The question then follows: “How does the apparent under-representation of (arts and) humanities-based disciplines affect expectations for SoTL, from norms for research design and methodology to the genre and style of its products?” (McKinney & Chick, 2010, p. 10). This paper, which frames the special issue looking at “SoTL through the lenses of the Arts and Humanities,” explores the difficulties with, and opportunities provided by, creating an inclusive teaching commons where the scholarly traditions of the arts and humanities are recognized for the value they bring to the SoTL research imaginary. Alors que la communauté des universitaires qui oeuvrent dans le domaine de l’avancement des connaissances en enseignement et en apprentissage (ACEA) s’est épanouie à travers le Canada et dans le monde, on constate l’éclosion d’un sentiment, parmi les humanistes, que le travail de l’ACEA a été dominé par les épistémologies, les philosophies et les méthodes de recherche des sciences sociales. C’est une opinion qui a été appuyée par les rédacteurs de revues sur l’ACEA et par les ressources consacrées à l’introduction des enseignants à l’ACEA. Pour citer Nancy Chick (2012) dans un livre récemment publié sur l’état actuel de l’ACEA dans diverses disciplines, « alors que de nombreux leaders éminents en matière d’ACEA proviennent des sciences humaines ..., le travail sur le terrain marginalise grandement les pratiques de leurs disciplines » (p. 15). Ce qui nous mène à la question suivante : « Comment l’apparente sous-représentation des disciplines du domaine des (arts et des) sciences humaines affecte-t-elle les attentes pour l’ACEA, allant des normes de recherche et de méthodologie au genre et au style de ses produits? » (McKinney & Chick, 2010, p. 10). Cet article, qui encadre le numéro spécial consacré à « L’ACEA à travers le prisme des arts et des sciences humaines », explore les difficultés qui existent à créer une commune d’enseignement inclusive ainsi que les opportunités créées par cette commune, où les traditions de recherche en arts et en sciences humaines sont reconnues pour la valeur qu’elles apportent à l’imaginaire de recherche de l’ACEA.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.037 | 0.125 |
| Scholarly communication | 0.043 | 0.058 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".