Student-staff partnerships for democracy, justice, and hope in higher education
Bibliographic record
Abstract
In this editorial, we reflect on the role of student-as-partners work in a world where human and humane ways of living are threatened by climate change, wars, the displacement of millions of people, and political moves for many governments away from democracy and toward the Right.In addition, it seems that Big Tech is taking over the world, controlling our data and often seeming more powerful than citizens' rights.Here we focus on two challenges that are foremost in our minds as we move into 2025: political change and the rise of generative AI as a pervasive technology that influences many aspects of our daily lives.We highlight how Students as Partners (SaP) can counter these challenges, especially in higher education.We reflect on potential paths forward for SaP and higher education, suggesting that during troubling times, SaP offers distinctive features supporting current faculty and students while presenting opportunities to shape a more just future in higher education.Specifically, we reflect on how democracy is at risk, how student partners perceive this threat, and how SaP work needs to build upon social justice principles, be grounded in theory, and continue creating spaces of reflection and support to foster a sense of hope.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".