A pedagogy of uncertainty: opening up fissures in the competence paradigm
Bibliographic record
Abstract
At UCLAN we are breaking the mould and have developed a blended learning MSci optometry programme which is the first blended learning course in optometric education in the UK and the first to use a practice-based online portfolio. \nOptometry has traditionally been taught as a 3‐year undergraduate programme. Upon successful graduation, students are required to complete a year in practice and meet the General Optical Council's (GOC) “ability to” core competencies. However, a recent study by the GOC found that 76% of students felt unprepared for professional practice with insufficient clinical experience and in response, the GOC is currently undertaking an educational strategic review. \nTo ensure the students receive high-quality clinical experience in the workplace, we have developed an online logbook and portfolio. Students log their experiences, learning points and reflections. The portfolio is closely monitored both by the student's mentor in practice and by academic staff. \nThe content and reflections logged by the students then helps to drive the face to face teaching, small group discussions and clinical experiences provided by the university.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.105 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".