The Promise and Perils of Comparing Nonprofit Data Across Borders
Bibliographic record
Abstract
The movement to democratize data and the advent of virtual research teams provides a near-perfect opportunity for an explosion of comparative nonprofit research. This manuscript provides a useful framework for scholars interested in utilizing comparative nonprofit data. By documenting how the lived context of the data is influenced by governmental, institutional, and social forces, we illustrate how effective comparative data work will involve knowing both the how (data details) and the why (institutional history) of the data elements. We offer three extended examples to illustrate the complexity of comparative data: the definition of nonprofit, the concept of governance, and the definition of financial liability. This approach provides a thoughtful path of not only careful empirical work but also the route to theoretical improvements as well. Furthermore, comparative work also leads the researcher to question assumptions and document the processes which shape the data, even within their singular context.
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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 | 0.525 | 0.768 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.026 | 0.074 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".