I’ll Show You Mine If You Show Me Yours! Portfolio Design in Two UK Veterinary Schools
Bibliographic record
Abstract
Portfolios are an attractive addition to the veterinary curriculum because they add to the assessment of competencies, are flexible, and encourage the development of reflective and lifelong learning skills. Veterinary schools at the University of Liverpool and the University of Nottingham, UK, have both recently introduced portfolios for year 1 undergraduate students. The key difference between the two institutions is that one uses the portfolio as a summative assessment, while the other allows formative assessment only. Advantages of assessing the portfolio include engagement in the process and the ability to examine the key professional skill of reflection. Advantages of using the portfolio for formative assessment only are a facilitation of honest self-criticism and that this approach encourages students to view the portfolio as a valuable professional and personal activity. Both portfolio systems will need to be closely analyzed in order to assess these perceived advantages, and the two institutions are learning from each other's experiences. Whether or not the portfolio is summatively assessed, student and tutor training and support are essential. Feedback from these stakeholders must also be analyzed and used to support and shape the portfolios as they become a central part of both veterinary curricula.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".