Bibliographic record
Abstract
Another year has passed quickly, and I’d like to take this moment to ask all of us to reflect upon the highlights of our professional and personal year. Too often we progress from deadline to deadline, significant dates and events, and once passed, we fail to remember and again enjoy these highlights that defined parts of our year. In our hometowns and on the broader global scale, some of us may remember events with mixed feelings and regret, whilst others rejoice with national pride. As such, our work as academics to disseminate important research findings and promote our cultural diversity and knowledges, is more important than ever. The IJHE continues to provide an important forum for the development of theory, the addressing of policy questions, and the dissemination of innovative practice in the field of tertiary education, specifically widening participation and lifelong learning. I am proud to be a part of the successful promotion of widening participation in tertiary education and lifelong learning.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.038 | 0.038 |
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".