(Re)Imagining higher education: an inspirational guide for academics
Bibliographic record
Abstract
We live in times of certain uncertainty with Higher Education in constant need of reflexive adaptation. The Reimagining Higher Education project, funded by the Association for Learning Development in Higher Education (ALDinHE), explored creatively and playfully the future of education. It invited the academic community to participate in workshops to reflect on the current status of Higher Education and, at the same time, to conceptualise what form a humane and integrated Learning Development, the holistic and sustainable fostering of academic literacies and practices, would take within that Higher Education system. The outcome is an open-source guide of Higher Education models, real and idealised, that potentially have the power to change perspectives and attitudes. In this short presentation, we (the project team) will showcase the guide, outlining what a more inclusive, empowering, and creative academia would look like. Our research participants have imaged the unimaginable: universities open, accessible, full of trust, care and laughter. Please join us to further reflect on the future of academia, with hope and positivity.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".