Open Universities 2.0: Leadership, Strategic Reset and The National Agenda
Bibliographic record
Abstract
The purpose of this article was to build upon empirical research, leadership theory, open university practice, and shifting global trends to provide open university leaders with a framework for strategic reset. Strategic reset is re- setting institutions priorities – making leadership choices – and taking actions to build competitive advantage, quality, and service for the future. The foundational pillars of strategic reset are centred around 1) digitalization – specifically online capacity; 2) setting new strategic priorities, and 3) establishing a national footprint that aligns with critical national employment and workforce development needs. A secondary theme that weaves itself through this article is the need for open universities to revitalize their commitment to innovation. The article concludes by offering some tactical actions that open university leaders can consider for framing strategic reset and setting new priorities for the future. These include: 1) streamlined open university models; 2) precision access; 3) building a national service footprint; 4) renewal of critical partnerships; and 5) exploring alternative funding models.
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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".