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Record W3101505332 · doi:10.3138/cjpe.69796

Application of an Evaluation Framework for Extra-Organizational Communities of Practice: Assessment and Refinement

2020· article· en· W3101505332 on OpenAlexaffvenueabout
Kaileah McKellar, Whitney Berta, Rhonda Cockerill, Donald C. Cole, Johanne Saint-Charles

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité du Québec à MontréalUniversity of Toronto
Fundersnot available
KeywordsKnowledge managementCommunity of practiceReflection (computer programming)Work (physics)Content analysisValue (mathematics)Qualitative researchPsychologySociologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Abstract: Communities of practice (CoPs) are groups of people who work together on an ongoing basis and share knowledge and expertise. CoPs exist both within and outside of organizations, although extra-organizational CoPs have received less evaluation attention. The primary objective of this study was to assess the applicability of a multi-level, multiple-value evaluation framework for extra-organizational CoPs. Qualitative interviews were conducted with an extra-organizational CoP—the Canadian Community of Practice in Ecosystems Approaches to Health (CoPEH-Canada). The evaluation framework oriented both the member interview guide and the deductive content analysis. The findings showed that the evaluation framework was sufficiently comprehensive to capture the values generated. Following reflection on these findings, challenges in its application and suggested revisions to the framework are provided; also discussed are limitations and strengths, evaluation research next steps, and the opportunities for future applications.

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.

How this classification was reachedexpand

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)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.395
GPT teacher head0.574
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes3
Has abstractyes

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