Extracurricular Partnerships as a Tool for Enhancing Graduate Employability
Bibliographic record
Abstract
The world of work is changing rapidly, with an increasing global demand for employees with higher-level skills. Employees need to have the right attitudes and aptitudes for work, possess work-relevant skills, and have relevant experience. Whilst universities are embedding employability into their curricula, partnerships outside of the taught curriculum provide additional, largely untapped, opportunities for students to develop these key skills and gain valuable work experience. Two extracurricular partnership opportunities were created for Bioscience undergraduates at the University of Leeds, UK: an educational research internships scheme, where students work in partnership with fellow students and academic staff on on-going educational projects, and Pop-Up Science, a unique, student-led public engagement volunteer scheme. Both schemes generate substantial benefits for all. They enhance student’s skills and employability, facilitate and enhance staff-student education practices and research, and engage the public with research in the Biosciences. Collectively, they demonstrate the extraordinary value and benefits accrued from developing extracurricular partnerships between students, staff, and the community.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".