Introduction
Bibliographic record
Abstract
Higher education is in crisis. Students are disengaged, lecturers are burned out, and universities seem more preoccupied with rankings and revenue than with knowledge and wellbeing. But rather than dwell on the problems, this book focuses on solutions—on hope. Bringing together a diverse range of educators and practitioners, this collection showcases real-world innovations that challenge the status quo and offer glimpses of a more humane and inspiring educational future. From rethinking systems and curriculum design to fostering imaginative collaboration and exploring the role of technology, the book highlights practical, hopeful interventions that are already making a difference. This is not a manifesto of complaints but an invitation to reimagine education. The contributors offer fresh perspectives from around the world, illustrating how small but meaningful changes can transform learning spaces, empower educators, and inspire students. For academics, teachers, administrators, and anyone invested in the future of education, this book serves as both a source of inspiration and a call to action. It is an evolving ecosystem of ideas - grounded in practice, rich with possibility, and rooted in radical hope. Now is the time to create the change we wish to see.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.410 | 0.241 |
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".