Social construction approach to nonprofit organization effectiveness : is it tenable?
Bibliographic record
Abstract
A nonprofit organization has multiple stakeholders, such as clients, employees, funders, licensing and accrediting bodies, and boards of directors. These stakeholders may have different criteria in evaluating the organization's effectiveness. Because of the lack of a market oriented bottom-line profit or loss criteria, leaders and researchers struggle with the concretization of the concept of nonprofit organization effectiveness. The principal aim of this research is to conceptually replicate part of a 1997 study using the latest theoretical approach to the analysis of nonprofit organization effectiveness, the social construction approach. Using sampling procedures and statistical data-analytic techniques, this research investigates the differences among stakeholders in their effectiveness judgments of nonprofit organizations. Survey responses from 174 stakeholders (49 board members, 55 employees, 36 funders and 34 beneficiaries) of 55 Montreal nonprofit organizations were analyzed. Consequences and implications for nonprofit organization managers were drawn from the analysis.
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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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.083 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".