From the Outside, Looking In with a Smile: A Summary and Discussion of CES's Credentialed Evaluator Designation
Bibliographic record
Abstract
Abstract: Drawing upon information presented in this issue, the article discusses the CES's Credentialed Evaluator designation using three frameworks: the Context, Inputs, Processes, and Products (CIPP) model to provide an overview of the program; developmental evaluation to examine key events and principles in the program's evolution; and adaptive action to raise issues both for the CES as it revises the program and for others around the world as they consider the possible benefits and risks of establishing evaluator credentialing programs. The Credentialed Evaluator designation has provided proof of concept for a viable evaluator credentialing system run by a voluntary organization of professional evaluators (VOPE). Specific considerations in moving forward in settings beyond Canada include the following: (a) the exercise of caution when using evaluator competencies to structure a credentialing program, (b) the importance of a perceived need for or value of a credential, (c) skillful attention to milieu, (d) finding qualified and committed people to develop and manage the program, and (e) ensuring that all stakeholders, including those outside the profession, are involved.
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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 | 0.071 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".