Enhancing Data-Driven Decisions by Improving Nonprofit Transparency and Collaboration: A Study of the Accessibility of Nonprofit Data in the Global Landscape
Bibliographic record
Abstract
This paper addresses the pressing need for enhanced transparency of nonprofit data to improve nonprofit collaboration, emphasizing the development of a global nonprofit database. Globally, there is a lack of transparency and accessibility to nonprofit data, which reduces the ability to make better decisions 1) between nonprofits as they work to collaborate and improve outcomes, 2) with nonprofit resource providers (foundations, volunteers, donors, company CSR programs, governments, grantees, etc.) when making decisions about who to support, and 3) with nonprofits as they attempt to reach those who need their services. By analyzing 34 nonprofit databases from the European Union, the United States, and Canada, this study explores the variation in data availability, quality, and transparency measures. Preliminary findings suggest that standardized and accessible nonprofit data can significantly improve decision-making, resource allocation, and strategic planning across these three groups. The study also highlights the potential of such a database to address the Sustainable Development Goals (SDGs) by identifying service provision gaps, optimizing collaboration, and informing data-driven advocacy and policy making. Through a comprehensive review of existing literature and a comparative case study methodology, this research aims to provide actionable recommendations for creating a more transparent, accountable, and collaborative global nonprofit sector. By creating a global nonprofit database that increases nonprofit transparency and makes data more accessible to decision makers, there will be 1) increased trust, 2) increased collaboration, 3) better decisions being made, and 4) better outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".