Editorial: Practitioner Research in Higher Education, 10 (1)
Bibliographic record
Abstract
This special edition of Practitioner Research in Higher Education emanates from papers presented at the 5th International Assessment in Higher Education Conference (https://aheconference.com/ahe-conference-2015) held in Birmingham, England, during June 2015. The biennial conference brought together practitioners and researchers to share their work and discuss current issues; aiding our understanding and the development of practice of assessment in higher education. The wide range of assessment areas explored at the conference included institutional change, diversity and inclusion, programme level assessment, students’ experiences of and responses to assessment and the assessment literacy of students and tutors. Master classes and keynote presentations by acknowledged experts in the field were complemented by papers and interactive posters from delegates. This provided a wealth of evidence that explored issues at national, institutional and grass-roots levels across many disciplines and phases. Presenters challenged current practices while offering developments to assessment policy and practice that would benefit higher education learners, leaders and teachers. International commonalities and variations in assessment were brought to the fore through the work of colleagues from Ireland, USA, Italy, Canada, Mexico, Australia, The Netherlands, Hong Kong, South Africa, Denmark, Vietnam, Spain, Sweden, Croatia, Norway, Scotland, Northern Ireland, Japan, Austria, New Zealand, Belgium and England. The range and diversity of the conference’s discussion continues within the 17 articles presented here.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".