Improving equality, diversity, and inclusion in staff-student partnerships
Bibliographic record
Abstract
Staff-student partnerships have become increasingly popular in educational spaces. Although staff-student partnerships have been shown to improve the inclusivity of assessment practices and increase curriculum engagement among underprivileged student partners, student partners still face barriers to access and face challenges when engaging in such opportunities. This makes it particularly important that all stakeholders involved, including institutions, staff, and students, actively work towards creating a cultural shift that celebrates and amplifies the minority voice. This paper is grounded both in current research and in evidence from a transdisciplinary staff-student partnership project from Imperial College London. During this project, team members conducted interviews and focus group discussions with students and staff from diverse backgrounds in order to develop a multimedia resource platform on best assessment practices. This paper provides a constructive framework, split into three themes, for promoting equality, diversity, and inclusion (EDI) in staff-student partnerships. The three overarching themes involve improving the inclusivity of selection processes, creating a flexible and trust-based working culture, and addressing power dynamics inherent in staff-student partnerships.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".