Strategic approaches to glocalising curriculum practice: Responding to faculty development needs and circumstances in diverse university contexts: Strategic approaches to glocalising curriculum practice
Bibliographic record
Abstract
University campuses around the world face significant challenges for engaging culturally diverse faculty and students with responsive programming (e.g., undergraduate, graduate, staff and faculty development programs). Policy documents espousing inclusion and the strategic institutional importance of local and global engagement, for example, are positive steps to foster institutional change. However, in practice, ad-hoc curricula renewal initiatives aimed at facilitating cultural diversity tend to be far less strategic, and with scant attention to research-informed and evidence-based scholarship. This paper attempts to address these complex challenges and provides insights toward a scholarly approach to glocalising curriculum practice for faculty development in multinational settings. In this context, data suggests that strategic institutional supports are key to glocalising curriculum practices. Further, a glocalised curriculum is inherently situated; socially and culturally mediated; and, is responsive to the professional learning needs and circumstances of educational leaders in diverse institutional contexts.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".