Discursive Resources for Intended Image in Nonprofit Organizations
Bibliographic record
Abstract
Abstract This paper contributes to the literature on nonprofit social ventures and discourse studies by identifying discursive resources leveraged to craft the intended image of a nonprofit organization. The intended organizational image plays a central role in the way stakeholders, notably sponsors, perceive a nonprofit organization. Nonetheless, we have little research aimed at identifying the resources and tools that organizations mobilize to structure their intended image, especially at the discourse level. Drawing on the analysis of the discourses adopted by a Brazilian not-for-profit organization, we propose a typology of five discursive resources: distinctiveness , identification , justification , storytelling , and validation . These resources operate as discursive-rhetoric tools that allow the organization to assert its uniqueness and shared values, legitimize its mission, narrate impact, and demonstrate credibility. Our findings contribute to a discourse-based understanding of nonprofit image construction by offering a framework that complements existing work in branding and legitimacy. The study also provides practical insights for nonprofit managers seeking to communicate authentically and efficiently with key stakeholders.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".