Management of Charitable Program Expense Ratios in the Charity Sector
Bibliographic record
Abstract
Abstract We examine factors likely to influence charity managers’ propensity to manage their charitable program expense ratios. To this end, we survey 202 Canadian charities. First, we ask managers whether they think a high charitable program expense ratio is important. The results suggest that managers are less concerned with charitable program expense ratios when there are no regulatory restrictions for this figure, but they are more (less) inclined to post a high charitable program expense ratio when the charity depends on private donations (relies on government grants). We also find a positive relationship between education level and managers’ perception of the importance of having a high charitable program expense ratio. Second, for managers who believe having a high charitable program expense ratio is important, we use a logit model to analyse their propensity to manage the ratio upward. We show that improving the management team's reputation, avoiding losing the organisation's charitable status and retaining or obtaining government grants propel charity managers to alter the ratio. However, managers with more experience in a management position in charities and those with higher levels of education are less likely to engage in this practice.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".