Data Aggregation: An Overview of Opportunities and Obstacles from the National to the Global
Bibliographic record
Abstract
Abstract It has been more than two decades since the publication of the United Nations Handbook on the System of National Accounts (Salamon & Anheier, 1994; Einarsson & Wijkström, 2019). This international standard setting approach for data collection, measurement, and reporting of national data has been joined by others including the United Nations, World Bank, OECD, and Open Government Partnership. Collectively these international data projects have increasingly improved their recognition and measurement of broad sets of third sector organizations, philanthropy, and volunteer work and provided important opportunities to produce foundational comparative data that bring new visibility and credibility to the third sector as well as enabling new research. This chapter provides an overview of current data aggregation efforts as well as the key issues that any data aggregation project must consider, including data quality, scope, commensurability, and durability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".