Organizational Self‐Censorship: Corporate Sponsorship, Nonprofit Funding, and the Educational Experience<sup>*</sup>
Bibliographic record
Abstract
La dépendance croissante du secteur privé envers la commandite a eu des conséquences sur la gouvernance des organismes sans but lucratif (OSBL). La recherche traditionnelle sur le financement a pris un angle d'observation essentiellement positif, montrant que les OSBL peuvent trouver des occasions d'influencer les intérêts de leurs bailleurs de fonds de manière à ce qu'ils deviennent plus compatibles avec la mission de l'OSBL. Dans cet article, les auteurs s'inspirent de ce travail en fournissant un examen plus nuancé de l'intermédiaire dans l'OSBL. Plus précisément, les auteurs introduisent une forme négative d'intermédiaire connue sous le nom d'autocensure organisationnelle. En examinant l'autocensure, ils montrent que les OSBL devraient plutôt redéfinir leurs propres buts de manière à attirer les bailleurs de fonds du secteur privé. Increasing reliance on corporate sponsorship has impacted the governance of nonprofit organizations. Traditional research on funding has taken a predominately positive vantage point, expressing that nonprofit organizations may find opportunities to influence their funder's interests such that they become more compatible with the nonprofit organization's mission. In this article, we build upon this work by providing a more nuanced examination of agency in the nonprofit organization. Specifically, we introduce a negative form of agency known as organizational self‐censorship. By examining self‐censorship, we reveal that nonprofit organizations may instead redefine their own goals in order to appeal to private sector funders.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".