Lifelong learning-centred community-based leadership development in higher education.
Bibliographic record
Abstract
This article proposes a Learning Sciences framework, set within a community-based leadership lens, emphasizing the implementation of a humanistic Lifelong Learning process, towards well-being in Higher Education (HE). What makes a community focused LL environment so difficult, is the longstanding business-based model that has dominated HE institutions over the past twenty years. It has produced a politically charged marketing-style mindset within HE administration that cascades to faculty and students. This cascade has contributed to mental health issues at several levels of HE. In response, HE administration and professional developmental bodies need to reframe leadership and professional development away from this dominant model, placing humanistic-focused development at the centre. The framework focuses on individual experiential development through the tripartite of LL, Social Emotional Learning (SEL) and Learning Communities (LC) through an Integration, Continuity and Engagement (ICE) process. This framework emphasises the reciprocal relationship that HE Administration must initiate and foster within the context of community development.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".