Articulating the supervisory functions of university supervisors in vocational education and training teaching internship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines supervisory practices in vocational education and training teaching internships within a blended-learning system. Using a multiple-case study of five triads made up of a university supervisor, a cooperative teacher from the workplace and a student teacher, practices were analysed through the lens of supervisory functions: support, mediation between theory and practice, collaboration, evaluation and internship management. Findings show that support and evaluation practices are dominant within triads. The management function is characterized by practices surrounding the selection and use of digital tools to supervise student teachers in a blended-learning system. The collaboration function appears to be inconsistent from one triad to another, while the mediation between theory and practice function plays a limited role in the practices of all supervisors, despite being identified as one of their main responsibilities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it