Transformative learning for university level students and advisors: a self study
Bibliographic record
Abstract
Professional practice evolves as individuals reflect and theorise about their experiences, and consider their practice in new ways. The purpose of this paper is to demonstrate how self-study can be used as an approach to interrogate the professional practice of a university advisor as they coach undergraduate students to develop meaningful resumes and job applications. Not only are the artefacts reconsidered but there is also a concurrent shift in identity and vocational persona. This paper uses the qualitative method of self-study where a practitioner provides vignettes and reflective writing to describe moments of transformative learning. These two sources of data were then evaluated using LaBoskey’s five characteristics of self-study. This research highlights how self-study supports a praxis and change in professional practice. The university advisor and undergraduate students when given the opportunity to consider their practices by looking to their motivations and assumptions it is possible to precipitate transformative learning. Self-study is an approach to an epistemology of practice that is rarely used outside the education field. However, it is an approach that has significant potential for investigating the practices in arrange of professional contexts where a praxial interrogation of the work is valued.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".