Emerging technologies in global communication
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how practitioners from Canada, the UK, Singapore, and the USA, university educational administration faculty from the USA, and the editor of a premier international journal of educational management engaged in a collaborative process to discover how to improve the preparation and practice of educational administration on a global basis. Design/methodology/approach An appreciative inquiry theoretical research perspective was used to discover a positive core of experiences that could add to cultural proficiency as it relates to educational administration. Findings In each cultural context, the central nature of the highpoint stories focused on appreciative ways to work with other people. The stories suggest that relationships and collaboration are at the heart of an educational administrator's practice. It was discovered that twenty‐first century educational administrators operating in a global context believed that they could become difference makers in education. Originality/value The findings from this study provide encouragement for extended global collaboration for practicing educational administrators as well as those who are responsible for their preparation. The findings also suggest that using collaborative technologies in the framework of a cross‐cultural educator partnership can enhance the preparation and practice in educational administration programs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".