Examining the impact of evaluation professionalization in Canada on the positioning, practice and employability of evaluators
Bibliographic record
Abstract
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.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".