Capacity building in nonprofit sport organizations: Development of a process model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the growing literature on organizational capacity in nonprofit and sport organizations, considerable gaps remain when the analysis shifts to building that capacity. This study proposes a comprehensive model of capacity building that recognizes the concepts and relationships involved in that process. The model was developed according to de Groot's (1969) interpretative-theoretical methodology, consisting of four phases that guide the collection and review of relevant literature: exploration, analysis, classification and explanation. As a comprehensive process, effective capacity building acknowledges that a capacity needs assessment occurs in response to some environmental stimulus. The subsequent identification of specific objectives for capacity building is followed by the generation and selection of a strategy(s) and consideration of multiple aspects of readiness to build capacity. The short-term impact and long-term maintenance of built capacity must be assessed following the implementation of the strategy(s) to build, with consideration of the implications for program and service delivery that address the initial stimulus. The model is described in the context of community sport organizations, however it is intended for broad application. Concepts and relationships presented in the model are relevant to the nonprofit voluntary organizational setting in general, while allowing for contextualization based on the unique factors and influences that may be involved in the process of building capacity. The paper concludes with consideration of how the model may be used in practice and directions for future research.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it