Bibliographic record
Abstract
To all our Journal’s readers and followers, an acknowledgement of how quickly a year passes, and a warm welcome to the final issue of 2023! This year has provided many events for us to contemplate and take into consideration when formulating research proposals responding to gaps in higher education. The ramifications of reduced funding allocations, time restrictions and the unspoken expectation to do ‘more with less,’ presents challenges to us all. I congratulate those who were successful in securing research funding in such competitive times and encourage those contemplating applying for funding to persevere with confidence. It is through our collective research efforts that we are able to disseminate best practices through our journal. In this issue, 14 papers on higher education practices by authors from the United States of America, Thailand, Belgium, Oman, China and Israel, share their focus on teaching, learning and assessment in education, and issues of training and human resources in industry and learning institutions.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.028 | 0.033 |
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".