Message from the Editor-in-Chief
Bibliographic record
Abstract
As this year draws to a close and we enter the Christmas and holiday season, I would like to extend my warmest wishes to you on behalf of the International Journal of Higher Education. The end of the year is always a natural moment to pause, to look back, and to look ahead. In higher education, we know that reflection is not a luxury but a necessity. This year, our community has once again navigated rapid change: the continuing impacts of digital transformation, evolving student needs and expectations, new policy directions in many countries, and ongoing questions about equity, inclusion, and the public value of higher education. Through your scholarship, you have helped to illuminate these issues with rigour, nuance, and care. For the final issue of Volume 14, we have 10 articles and strong representation from researchers from the UK, the USA, China, and Canada.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.037 | 0.041 |
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".