What Counts? How to use Different Sources of NGO Data
Bibliographic record
Abstract
Abstract The quantitative data sources for NGO scholars are increasing, introducing new possibilities for our understanding of the global NGO population. The most frequently used data sources tend to privilege larger NGOs located in more politically open countries. We highlight two developments. First, we introduce a new Global Nonprofit Registry of Data Sources (GRNDS) dataset. GRNDS documents the information that governments collect and release to reveal variations in the data environment. Second, new sources of information from social media and donation platforms avoid the filtering and curation of reports from nonprofit regulators. These include Twitter, Google Trends, and new data from #GivingTuesday. Together, this richer information on cross-national variation in reporting and quickly available digital data should help research build a richer picture of the global NGO sector.
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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 | 0.083 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.030 | 0.036 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".