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Examining the impact of evaluation professionalization in Canada on the positioning, practice and employability of evaluators

2024· article· en· W6962970895 on OpenAlexaboutno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsProfessionalizationCredentialEmployabilityConstruct (python library)Field (mathematics)Focus groupProfessional developmentBody of knowledgeQualitative research

Abstract

fetched live from OpenAlex

This thesis presents an exploration of the impact of evaluation professionalization through credentialization, with a particular focus on the Canadian context, where the Canadian Evaluation Society (CES) has pioneered the Professional Designation Program (PDP) incorporating the 'CE' designation. Employing a mixed, multi-methods approach, this study sought to address research questions concerning the positioning, practice and employability of evaluators. The overarching research question examined the effects of evaluation professionalization in Canada, while sub-research questions compared the Canadian model to international counterparts, delved into the experiences of 'CE' designated evaluators, and drew insights from the application of a credential for evaluation in the Canadian context. Research findings revealed multifaceted impacts, highlighting that evaluation is a social construct affecting professionalization, identifying barriers to qualification-based approaches, and emphasizing the multidimensionality of professionalization's impact in Canada. The study's outcomes contribute valuable insights to the field of evaluation professionalization, providing guidance for practitioners, policymakers, and professional associations in Canada and globally. Ultimately, this research advances our understanding of the complexities surrounding evaluation professionalization and seeks to bolster the evaluation profession's development on a broader scale.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.166
GPT teacher head0.438
Teacher spread0.272 · 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.

Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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