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Record W2087036401 · doi:10.1080/02602930902862842

Bases of competence: an instrument for self and institutional assessment

2009· article· en· W2087036401 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

VenueAssessment & Evaluation in Higher Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCompetence (human resources)PsychologySelf-assessmentMedical educationPedagogyMathematics educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

The Bases of Competence model provides a general framework for learner‐centred skill development and programme‐focused outcomes assessment. Based on previous research, the Bases of Competence model describes 17 skills and four base competencies important to graduates to achieve high performance in the workplace. Taking this work from research to relevant educational application as a tool for student self‐assessment and institutional outcomes assessment is the focus of this paper. Results from a multi‐year, multi‐course assessment initiative indicate that students rate themselves stronger in the foundation base competencies of Communicating and Managing Self, and weaker in more complex competencies of Managing People and Tasks and Mobilising Innovation and Change. Comparisons of skill confidence within each base competence as well as between year, student level, gender and beginning versus end of semester are presented as well. These results are discussed and suggestions made for programme design.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.486
Teacher spread0.369 · 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