Unnatural Selection: An Examination of the Fraser Institute’s Performance Index for the Donner Canadian Foundation Awards for Excellence in the Delivery of Social Services
Bibliographic record
Abstract
Abstract: Using Darwinian metaphors, this article examines a relatively new quantitative performance evaluation tool for Canadian nonprofits: the Fraser Institute’s Performance Index for the Donner Canadian Foundation Awards for Excellence in the Delivery of Social Services. Several aspects of the Index, including strategic management, outcome monitoring, and income independence, are analyzed in light of their ability to accurately evaluate excellence in performance and their potential unsuitability for many non-profits. A theme throughout this discussion is the interaction of the “business-like” approaches favoured by the Index and the fundamentally different nature of non-profit perspectives. Particular attention is paid to the difference between organizations that share a profit motive and non-profits that have diverse organization-specific missions. Context-specific mission-driven approaches to non-profit performance evaluation are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".