Preparing Nonprofits for New Accountability Demands
Bibliographic record
Abstract
Abstract: Although the role of nonprofits in Canadian society has always been important, the sector now plays a greater role as more and more government services have been transferred to the sector as part of the move toward governance over government. Complementing this changing role is the need, within both government and the sector itself, to enhance accountability and transparency based on evidence. Although program evaluation offers a viable tool to achieve these ends, a great deal of apprehension must be overcome, as must the lack of a sound infrastructure of technical leadership capacity within the nonprofit sector. This article examines these challenges within the context of nonprofits in the social/health or human services areas. It suggests building evaluation capacity through a particular approach to evaluation courses. It examines the role of the nonprofits, an approach to teaching, and the role of funders and educational institutions in developing this capacity. The capacity to conduct evaluation has been ignored by funders, who may mandate an evaluation with the unrealistic intent of it providing accountability.
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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 | 0.065 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".