Telling our Curriculum Review Leadership Story: The Beginning, the Middle and Still Editing the End
Bibliographic record
Abstract
As post-secondary institutions embrace quality assurance strategies and frameworks, who is leading the work both at the institutional and program levels? As these leaders structure processes and engage faculty members in a curriculum review as part of a quality assurance framework, what are the attributes of their work that impact the success of the review process? As an institutional and a faculty leader charged with leading two curriculum reviews in a School, we share our leadership stories. Using Schon’s (1983) reflective framework, reflection- in- action and reflection- on -action, along with Killion and Todnem (1991), reflection- for -action, we identify key attributes of leadership, as well as what we have learned in moving forward with quality assurance work, to tell our story.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".