Bibliographic record
Abstract
This chapter examines evaluation utilization in organizations, using the extensive experience gained in the Canadian federal government with evaluation units in departments. It presents data and insight gained from evaluation practice in Canadian federal departments and agencies and aims to propose a conceptual supply/demand model to explain the nature and extent of evaluation utilization in organizations. The model suggests that evaluation utilization in an organization is best enhanced by evaluators proactively balancing the demand for evaluative information with its supply. Evaluation in the government of Canada has been part of the management of departments since 1977. Management styles and systems change over time both within organizations and across government. A credible evaluation function that is seen as a useful and important part of the management of an organization can influence the articulation and the demand for performance information.
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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 | 0.070 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| 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".