A Machine‐Learning Approach to Understanding Performance of Canadian Nonprofit Sport Organizations
Bibliographic record
Abstract
ABSTRACT Previous approaches to model the performance of nonprofit organizations and their determinants largely rely on linearity and monotonicity assumptions. This research note makes a methodological contribution by jointly using descriptive and predictive models, particularly advanced machine‐learning algorithms that allow for the consideration of non‐linear or non‐monotonic relationships, to understand the relevance of factors associated with the performance of nonprofit sport clubs as well as the nature of relationships. Data were collected via an online survey with 126 representatives of Canadian sport clubs, in which four performance domains were considered: Member relationship, service quality, financial stability, and sporting success. Explanatory linear regressions and four machine‐learning models (i.e., ridge regression, bagged regression, random forest, and gradient boosting machine) are used. The results reveal that machine‐learning models increase the explanatory power compared to linear models. The random forest outperforms the other models in terms of root mean squared error and, partly, mean absolute error, and R square (even though absolute levels of R square are low at times, particularly for financial stability and sporting success, where the presence or absence of a high‐volume donor or high‐performance sports mission might help or hinder performance). Non‐linear relationships are found for several predictors across the four dimensions that were considered, such as the use of outside knowledge, trust, coopetition, age, and tenure of the club representative. We showcase the use of joint computational techniques in nonprofit research to serve two relevant goals: enhance the explanatory power and maintain the interpretability of predictive models.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".