Bibliographic record
Abstract
This case study considers a students-as-partners’ research project that aimed to develop technologically-driven tools to enhance teaching and learning in higher education. It focuses on how the project enabled student participants to gain real world research skills and experience. We present reflections from both a student and a staff perspective and propose START (Support, Time, Adapt, Risks, Trust) as an approach to engage students to gain real-world research skills. Support refers to providing support for skills gaps and learning in an applied setting. Time refers to providing time to settle into the project and develop confidence, including realistic timeframes and deadlines. Adapt refers to giving students the space to develop not only the required skills but also the tools to develop their own abilities and confidence through a supportive, flexible and open environment. Risks refers to taking risks for example in terms of roles, responsibilities and leadership. Trust refers to providing guidance and encouragement that will allow students to achieve on their own and take shared ownership.
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 | 0.040 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".