Is it time to clean up US tax-exempt nonprofit reporting?
Bibliographic record
Abstract
Purpose US tax-exempt nonprofits are chronically underdeveloped when it comes to reporting, communicating and comparing the value they create. This paper aims to explore an approach to address these reporting and disclosure issues, for the purpose of sustainability and impact. Design/methodology/approach First, the authors ask and then answer: is it time to clean up US tax-exempt nonprofit reporting? Second, the authors develop a theoretical argument, based on commensuration of impact, for a specific tax-exempt integrated report (IR), to compare the value of tax-exempt nonprofits. Third, this study offers an example of this tax-exempt IR in practice. Findings First, this study evidences the need for a drastic shift in the expectations and reporting practices of US tax-exempt nonprofits. Second, this study offers an IR framework that responds to recent scholarly calls to address organizational accountability boundaries and impact assessment in the nonprofit sector. Third, this contributes to sustainability policy conversation by mapping out an approach that US tax-exempt nonprofits could deploy to speed up the implementation of sustainable solutions (Sustainable Development Goal [SDG] 17). Practical implications This study contributes to sustainability conversation by closing with a discussion of why policymakers, managers and scholars should continue to push for maximum impact from US tax-exempt nonprofits. If addressing the UN SDGs is a desired outcome, then there is an immediate need for change in the way US nonprofits report what they do. This study suggests that learning from the European Union reporting practices and regulations will facilitate a move toward improved reliability, comparability and impact from US nonprofits. Social implications The aim of this paper was to present a disclosure framework that provides reliable and comparable information of the value created by tax-exempt nonprofits. This principle-based framework is rooted in the IR literature and extends into the prosocial world of tax-exempt nonprofits, recognizing that is it goes farther than simply being a framework; it is a social process. Originality/value This paper responds to recent calls for more oversight and comparison disclosure mechanisms of US tax-exempt nonprofits, for the purpose of reducing social or environmental inequality. The framework makes an important contribution to the field of sustainability accounting, in that it promotes a principle-based approach for measuring and regulating tax-exempt nonprofits, in a way that motivates oversight and comparison of sustainability-related practices.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".