Inquire, Imagine, Innovate: A Scholarly Approach to Curriculum Practice
Bibliographic record
Abstract
This paper describes the development of a three-phase approach our team of educational developers finds useful in curriculum projects in our Teaching and Learning Centre. Informed by the literature on the importance of flexibility and iteration (Knight, 2001; Wolf, 2007) and an orientation towards Appreciative Inquiry (Srivastra & Cooperrider, 1990), we contextualize our work in relation to others in the Canadian educational development landscape. Additionally, we highlight the importance of recognizing micro, meso, and macro levels of influence in institutions of higher education (Poole & Simmons, 2013). We describe our Inquire, Imagine, and Innovate, or 3-I, model for curriculum consultation, positioned by fictionalized vignettes demonstrating how each phase is applied. We conclude the paper by indicating where we are continuing to develop this work. Dans cet article, nous décrivons l’élaboration d’une approche en trois phases que notre équipe de concepteurs pédagogiques juge utile pour les programmes de notre centre d’enseignement et d’apprentissage. À partir de la recherche sur l’importance de la flexibilité et l’itération (Knight, 2001; Wolf, 2007) et d’un penchant pour l’interrogation appréciative (Srivastva et Cooperrider, 1990), nous replaçons notre recherche dans le contexte d’autres travaux dans le domaine du perfectionnement de l’enseignement au Canada. De plus, nous insistons sur l’importance de reconnaître les micro-, macro- et méso-niveaux d’influence dans les établissements d’enseignement supérieur (Poole et Simmons, 2013). Nous décrivons notre modèle 3-I – Interrogation, Imagination, Innovation – pour la consultation sur les programmes, en montrant, au moyen de fictions sur vignettes, comment chaque phrase se déroule. En conclusion, nous indiquons quelles sont les suites que nous donnons à ce travail.
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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 | 0.056 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.019 | 0.071 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".