ACCREDITATION IN THE EU: A FIRST STEP IN BENCHMARKING GERONTOLOGY PROGRAMS
Bibliographic record
Abstract
The Dutch and Flemish accreditation systems of higher education regulate educational quality of programs. Foundations include Dublin descriptors and ten general competencies of higher education. AGEC is an important body for benchmarking gerontology programs in an international context. This will enhance improve faculty and student movement and exchange. For that reason, a Dutch BSc program in Applied Gerontology applies Associaton for Gerontology in Higher Education (AGHE) competencies in 3 different ways: (1) as input of programs’ core competencies; (2) as core for the development of learning outcomes; and (3) as input for learning objectives in classes. We present our method of mapping Dublin Descriptors; general competencies of higher education and the AGHE competencies on program and class levels. Our method promotes unequivocal use of AGHE competences in the international arena of gerontology education. It may serve as a point of reference for other European programs in gerontology.
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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 | 0.111 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".