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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".