Mentoring and Role Modelling in Educational Administration and Leadership: Neoliberal/globalisation, Cross-cultural and Transcultural Issues
Bibliographic record
Abstract
Many, if not most, of my writing comes from experience, as it does for many scholars -either observing situations and events, being among those involved in these, and the many discussions with students and colleagues about the experiences they have gone through in many countries I have visited or worked in.This topic of mentoring and the related role of role modelling initially came to me shortly after doing my doctorate in a mentoring mode with Christopher Hodgkinson in Canada, and my ideas about this were reinforced when I was mentored in informal postdoctoral work with Wolfgang Mommsen in Germany in the 1990s.Since that time, I have mentored some of my doctoral students, particularly in the last few years working with several in the Arabian Gulf.It was time for me to revisit my research and understanding of this topic after going through several years of research trips, guest lecturing and collaborative projects in Western, Central and Eastern Europe, followed by several years in the Gulf learning about the embeddedness of such roles in
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".