The potential of student as partners approaches for humanitarian developments
Bibliographic record
Abstract
This article explores the potential for students-as-partners models developed in the scholarship of teaching and learning and educational development fields to be expanded to new agendas such as humanitarian developments and other agendas related to the so-called civic university. There is a growing appetite for students and staff to work in partnership due to the mutual benefits for both parties (Mapstone et al., 2017), yet the majority of the published works on students as partners is almost exclusively reporting upon partnership activities relating to curriculum and wider student experience developments in higher education. This paper explores the literature on best practice for working with students as partners in order to create new recommendations for how the students-as-partners model can be applied successfully for community and humanitarian development projects, rather than curricular, teaching, or research projects By drawing on literature from student voice, student engagement in quality assurance, and co-design, this paper will highlight the great potential of student-staff partnerships for addressing other development agendas globally.
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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.047 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".