Bibliographic record
Abstract
To cite this article: Dr Ingrid Harrington. (2022). Message from the Editor-in-Chief. International Journal of Higher Education, 11(3), p0-1. https://doi.org/10.5430/ijhe.v11n3p0 doi:10.5430/ijhe.v11n3p0 URL: https://doi.org/10.5430/ijhe.v11n3p0 As higher education institutions continue to negotiate effective ways forward embracing on-line learning pedagogies due to the COVID-19 pandemic, we now read many of the findings from on-going research into the effectiveness and impact these changes have made to student learning. Higher education institutions are at the very nexus of career-focussed education, for students seeking qualifications to contribute positively to their community. Recognising the strength of academia and the challenges that plague access to a range of reliable resources, ensure that policy-makers and educators alike, continue to review best practices in order to provide the innovative delivery of pedagogical excellence. We are proud to present this issue with contributions and perspectives from the USA, Cameroon, Ghana, Nigeria, Oman, South Africa, Spain, Kuwait, Uganda and Israel. This issue has a strong focus on learner pedagogy, gender performance, student teacher experiences, and post-COVID adaptations. 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.
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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 | 0.006 | 0.051 |
| 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 | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.094 | 0.102 |
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".