Chapter 2. Surviving not Thriving: racially minoritized female trailblazers working in UK Higher Education institutions
Bibliographic record
Abstract
This volume of Leading Global Excellence in Pedagogy brings together a diverse collection of thought-provoking papers that spotlight transformative and innovative teaching practices. Authored by nationally recognised educators from universities across the UK, Canada, the USA, and Australia, the book showcases powerful methodologies that are shaping the future of teaching and learning in higher education. Contributors explore forward-thinking strategies including digital transformation, lifelong learning, upskilling, micro-credentialing, competency-based education, inclusive teaching, and support for mental health and well-being offering rich insights into pedagogical innovations across disciplines and learning contexts whilst providing practical approaches to designing impactful educational experiences. This book is an essential resource for academics, teachers, researchers, and students seeking to adopt evidence-informed practices and lead meaningful educational change. It highlights the importance of thoughtful planning, equity, and adaptability in sustaining high-quality teaching in today’s evolving academic landscape. Both editors are National Teaching Fellows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".