Institutional readiness and grant success among public recreation agencies
Bibliographic record
Abstract
The financing of public recreation is diversifying. In the past, recreation agencies have used numerous strategies to address financial issues, with varying degrees of success. Such strategies have included retrenching programmes, implementing user fees, reducing staff and relying on volunteers. In North America, recreation managers have also begun to engage in fund raising efforts, including grant seeking. In 2003, recreation, sport, art and culture agencies combined received 14.7% or $2,102,824.00 of foundation grants in the United States, excluding federal and state grants. In order for the field of recreation to be successful at securing foundation grants, empirical research is needed to establish a sound knowledge base. The purpose of this study was two-fold: to validate hypothesized measures of institutional readiness, a concept originating from philanthropic studies, and to determine the strength of institutional readiness in predicting the number of foundation grants received by park and recreation agencies. Contrary to the literature, only two measures of institutional readiness (working with a board of directors and using a case statement) were found to predict success in receiving foundation grants. None the less, fund raising strategies, in particular soliciting foundation grants, represent a strategy for recreation managers to consider when faced with financial dilemmas.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".