Bibliographic record
Abstract
In 2022, the Staff Educational Development Association (SEDA) developed the Student Partnership Impact Award (SPIA), providing students and recent alumni with an opportunity to be professionally accredited for their leadership abilities through partnership. SEDA is a professional association for educational developers based in the UK. The SPIA award aims to expand SEDA’s community of educational developers by reaching out to other national and international students and staff working in partnership. SEDA’s development team, of which I am part of, carried out a review of the award procedures for quality assurance purposes. The review process found a lack of leadership narrative in unsuccessful applications. This stemmed from applicants not being ultimately responsible for a project and, as a result, these applicants subsequently seemed unable to claim any leadership, which set the tone of applicants being subordinates to the staff project lead. These findings raised for me and the development team further questions about students’ exposure to leadership skills development and students’ ability to recognise their own leadership skills as part of their employability skills development. This case study explores thematic factors affecting students’ ability to confidently articulate themselves as leaders in a student partnership setting and what we can do as staff to support students in developing those graduate attributes. It also provides reflections and ideas for colleagues considering putting students forward for professional accreditation or potentially developing their own awards scheme.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.019 |
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".