Organizational Capacity and Anticipated Growth in Nonprofit Voluntary Community Sport Organizations
Bibliographic record
Abstract
Using a multidimensional framework we investigated the critical elements of capacity in community sport organizations (CSOs) and their impact on anticipated change in participation levels in the near future. A sample of 336 presidents of CSOs in 20 sports across the province of Ontario, Canada, completed a web-based survey measuring the extent of various elements of human resources capacity (e.g., volunteer attitude, skills, development), infrastructure capacity (formalization, communication, facilities), finance capacity (stable revenues and expenses, financial management, alternate sources), planning capacity, and external relationships capacity (personal connections, engaged/dependable partnerships, bureaucratic/imbalanced partnerships). The survey also measured anticipated change in participation levels over the next three years. Elements representing all five dimensions were significantly associated with change in participation. Planning mediated the impact of sufficient volunteers on this outcome, suggesting that an adequate number of volunteers are necessary to undertake and implement creative long-term planning, which is necessary for increased participation levels in CSOs. The findings highlight the rich information that may be generated from a multidimensional and context-specific perspective on organizational capacity, and indicate implications for building capacity in CSOs. Directions for further research are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".