Evaluation Practice in Canada: Results of a National Survey
Bibliographic record
Abstract
Abstract: This article reports on the results of a national survey that describes the professional and practice profiles of program evaluators in Canada, their views of their working conditions, and their sense of belonging to the field of evaluation. The data were collected between May and July 2005 via a Web survey, and 1,005 respondents filled out questionnaires. Among them, 647 indicated that they were internal or external evaluation producers, the others being evaluation users, students, or researchers. The results raise several issues. First, much of the evaluation work being done in Canada appears to be driven by accountability requirements, and secondarily by an appetite for program improvement or reconsideration. Second, voluntary certification, while quite widely supported, may create or encounter significant challenges in attempting to achieve professionalization goals. Third, the survey documents the need for professional training and the low levels of satisfaction with the training received to meet the requirements of evaluation positions. Finally, based on the current configuration of the population of active evaluators, on the intent of a majority of young evaluators to leave the field in the next few years, and on the training required in evaluation, the profession is not currently in a position to sustain itself through the renewal of a stable, capable, and committed workforce. Taken together, these results suggest a need for reflection and action on the future development of the profession.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".