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Record W2727753050 · doi:10.1093/geroni/igx004.3953

ACCREDITATION IN THE EU: A FIRST STEP IN BENCHMARKING GERONTOLOGY PROGRAMS

2017· article· en· W2727753050 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsHuntington University
Fundersnot available
KeywordsAccreditationBenchmarkingFlemishContext (archaeology)Core competencyHigher educationMedical educationClass (philosophy)MedicinePolitical scienceComputer scienceManagementArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.124
GPT teacher head0.397
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it