Bibliographic record
Abstract
Welcome to Volume 14, Issue 5 of the International Journal of Higher Education (IJHE). This latest issue arrives at a pivotal moment for higher education worldwide, as institutions grapple with profound challenges stemming from globalisation, digital transformation, equity imperatives, and evolving workforce demands. IJHE remains committed to bridging research, policy and practice — offering an international, interdisciplinary forum for scholarship on teaching, learning, leadership, strategy and innovation in higher education.In this issue, readers will find a compelling mix of empirical studies, critical reviews and theoretical contributions that address core themes such as student engagement, pedagogical innovation, institutional accountability and inclusive access. The articles span a diverse set of national and institutional contexts, reflecting the journal’s global scope and its emphasis on evidence-informed solutions to enduring and emergent issues.We are proud to present this issue with contributions and perspectives from the UK, Brazil, Egypt, Democratic Republic of Congo, Belgium, and Portugal. This issue has a strong focus on learner pedagogy, student and teacher experiences, and social, cultural, and economic influence on student retention and performance. Research in these areas provide interesting and informative reading, on how global educators continue with their core business of delivering relevant and meaningful education to their students.
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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.031 |
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".