Bibliographic record
Abstract
This chapter focuses on government policy and the importance of executive leaders in influencing, shaping and translating this policy into constructive practice while working within regulatory frameworks. The latter frameworks are not covered in detail, as the focus is very much on the higher-level global policy trends, highlighting what these trends mean for university leaders focused on long-term sustainability and, in many instances, short-term survival – particularly in the cash-strapped post-pandemic environment. Illustrations from Australia, the UK and Canada bring to life the national and local contexts in which conversations and debate about higher education policy occurred, and the chapter considers government shifts in attention to, for example, lifelong learning, diversity and inclusion, and performance-based funding. Essentially, this chapter considers the extent to which recent shifts – particularly those arising from the ongoing post-pandemic introduction of new policy regulatory frameworks around, for example, overseas recruitment – represent significant change or, alternatively, continuity. Integrated in the discussion on policy contexts are a range of case studies from respected global university executive staff, illustrating how they, as key strategic leaders, are responding to the new legislation and frameworks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".