A national approach
Bibliographic record
Abstract
This chapter explores programme leadership in a collaborative, multi-institutional context arguing cross-institutional connection offers programme leaders (PLs) opportunities for greater depth of learning and opportunities to build networks and platforms that enhance both the value and prestige of their leadership position. The chapter reflects on the authors&s; work in Scotland establishing a cross-sector ‘Collaborative Cluster’ tasked with better understanding the diversity of approaches taken to programme leadership, sharing experience and practical resources and identifying areas for collaborative learning and development. Bringing together the PLs and those responsible for enhancing institutional practice and support, the Collaborative Cluster succeeded in developing a Scotland-wide schedule of work to address some key challenges including leading without authority, role confusion, working with programme-level data and a lack of role-specific opportunities for professional development. Exploring the work of the Cluster, the chapter offers evidence of value, success and inspiration for action in the following three key areas: Vignettes of practice from the frontline of programme leadership Collaboration for developing institutional and individual conversation to initiate changed practice Recognition and reward for those in PL roles Webb&s;s Canadian practitioner response reflects on the value of this collaborative approach to understanding and supporting programme leadership.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.031 |
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".