Bibliographic record
Abstract
A complementary volume to Dilly Fung’s A Connected Curriculum for Higher Education (2017), this book explores ‘research-based education’ as applied in practice within the higher education sector. A collection of 15 chapters followed by illustrative vignettes, it showcases approaches to engaging students actively with research and enquiry across disciplines. It begins with one institution’s creative approach to research-based education – UCL’s Connected Curriculum, a conceptual framework for integrating research-based education into all taught programmes of study – and branches out to show how aspects of the framework can apply to practice across a variety of institutions in a range of national settings. The 15 chapters are provided by a diverse range of authors who all explore research-based education in their own way. Some chapters are firmly based in a subject-discipline – including art history, biochemistry, education, engineering, fashion and design, healthcare, and veterinary sciences – while others reach across geopolitical regions, such as Australia, Canada, China, England, Scotland and South Africa. The final chapter offers 12 short vignettes of practice to highlight how engaging students with research and enquiry can enrich their learning experiences, preparing them not only for more advanced academic learning, but also for professional roles in complex, rapidly changing social contexts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 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".