The Laurentian University CAE Coffeehouse
Bibliographic record
Abstract
Laurentian University’s Teaching and Innovation team recently created the virtual CAE Coffeehouse, an online repository of a variety of faculty development resources. The online CAE Coffeehouse supports the physical in-person Faculty Learning Community through web-streamed workshops, drop-in hours, a teaching “book club”, individual consultation sessions, and various departmental outreach initiatives. This article presents the characteristics of a Faculty Learning Community (FLC), as defined by Cox (2004, 2013), and a Professional Learning Community (PLC), as defined by Cherrington et al., (2018), and describes how the development of the CAE Coffeehouse builds upon these principles to extend the community into a virtual space. Récemment, l’équipe Enseignement et Innovation de l’Université Laurentienne a créé, sous une forme virtuelle, la CAE Coffeehouse, un répertoire en ligne proposant un éventail de ressources en perfectionnement des corps professoraux. Cet outil en ligne vient en appui à la communauté d’apprentissage professorale – laquelle prend quant à elle une forme physique, présentielle – en offrant des ateliers en direct sur le web, des heures de rencontre sans rendez-vous, un club de lecture sur l’enseignement, des séances de consultation individuelles et différentes activités d’information départementale. Dans notre article, nous présentons les notions de communauté d’apprentissage professoral, telle que définie par Cox (2004, 2013), et de communauté d’apprentissage professionnel, telle que définie par Cherrington et al. (2018). Ensuite, nous montrons comment l’élaboration de la CAE Coffeehouse se fonde sur de tels principes pour étendre la portée de la communauté jusque dans l’univers virtuel.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.015 |
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".