The Evolving Market for Systematic Evaluation in Canada
Bibliographic record
Abstract
Abstract The professionalization of evaluation and the need for educational programs and accreditation has taken on increasing importance in Canada over the 2000s. Central to these issues is the need to understand the nature of the evaluation industry, both from the “supply” and “demand” side. While there have been fragmented discussions about the nature of evaluation in the federal government (e.g., Lahey, 2010; Segsworth, 2005) and the provinces and territories (e.g., Gauthier et al., 2009), there remains a dearth of information about the nature and behavior of the evaluation industry in Canada as a whole. This chapter offers an exploratory investigation into the evaluation industry in Canada, examining both the demand and the supply side, along with a historical context that serves as a backdrop in understanding the current structure of the Canadian industry. The study examines how various factors affect market behavior, and reflects on considerations for the future of the Canadian evaluation industry.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".