Toward an SDG‐Based Typology for US Nonprofits
Bibliographic record
Abstract
ABSTRACT The Sustainable Development Goals (SDGs) represent an emerging institutional logic that nonprofits must navigate alongside existing sector‐specific frameworks. Drawing on institutional logics and organizational hybridity theories, we examine how nonprofits incorporate SDGs into their missions and what this reveals about managing institutional complexity. Using a large language model to analyze nearly 50,000 US nonprofit mission statements, we develop an SDG‐based typology that captures mission hybridity—a key dimension existing classification systems obscure. We find that nonprofits embedded in strong professional logics (e.g., healthcare, education) show concentrated SDG alignment, while those spanning multiple institutional spheres demonstrate diverse engagement patterns. Mission statements relate to an average of 1.94 SDGs, with modest intergoal correlations suggesting context‐specific rather than template‐driven implementation strategies. Our study advances understanding of how organizations translate global frameworks through existing institutional arrangements, provides a quantitative measure of mission complexity, and offers practical insights for nonprofit alignment with global sustainability priorities.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".