The Profile of the Supervising Lecturer: On the Association between Supervision Outputs and the Nature of the Supervision
Bibliographic record
Abstract
This study is a pioneer study that seeks to explore the association between supervision outputs and the nature of the supervision (by e-mail, face-to-face, the student’s independence). In addition, the association between the supervising lecturer’s personal background (gender, age), professional background (faculty, tenure, excellence in teaching), and the supervision outputs (articles written, presentations at conferences) and nature of the supervision is explored. The purpose of the study is to explore items in depth so that they can be used in developing a questionnaire exploring the association between supervision outputs and the nature of the supervision. The research findings show, for various items, significant interactions between age and gender, excellence in teaching, and tenure. Significant interactions were also found between gender and faculty, as well as between age and excellence in teaching. Differences were also found between faculties. The findings of this study might have practical consequences for establishing the output-guided association in research supervision, as well as for establishing the practical association between the nature of the supervision and the lecturer’s personal background, professional background, the supervision outputs, and the nature of the supervision. These findings constitute a foundation for methodological thinking concerning supervision by staff members, a subject that has considerable meaning for the initial steps taken by young student researchers and for the systematic establishment of the pattern of academic supervision, which although not frontal teaching may be even more demanding.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".