Editorial: Collaboration in higher education: Partnering with students, colleagues and external stakeholders
Bibliographic record
Abstract
Welcome to this Special Issue of the Journal of University Teaching and Learning Practice (JUTLP). This editorial provides an overview of Collaboration in Higher Education. Humans are social, inter-dependent beings, needing to be and communicate with each other. Being with other people provides an opportunity to grow and develop, creating a sense of self and identity. Together we construct, structure and restructure the stories that build the larger narratives of who we are, what we do and how we live, act and behave as people, professionals and larger communities. It is through our collaborations that we come together, and construct meaning and ourselves. As Higher Education continues to exclude and sideline, as it constrains and removes spaces and places for collaboration between service staff, faculty and students within institutions, between institutions, and with other stakeholders, there is a need to rediscover the power of collaboration. The articles included, build on practical experience, research data, personal and collective reflections, to outline how the contributors have navigated this tension to create spaces of voice and hope. Presented are case studies that are boundary crossing: across disciplinary boundaries; cross-institution collaboration; cross-boundary working; pedagogical co-creation and the re-conceptualising of learning; and students as partners, co-researchers and co-authors. Together they showcase refreshed notions of collegiality and collaboration in Higher Education that support new and more nuanced, and dynamic models of co-creation. We hope the Special Issue helps seed an ecology of collaborative practice for social justice – a more humane academia.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".