Bibliographic record
Abstract
The Common Impact Data Standard provides a way to exchange information about impact by Social Purpose Organizations using open Web standards. If it is to be broadly accepted, it must communicate information in formats that are useful to its main audiences. In practice, that means that the Common Impact Data Standard must be well situated among the standards that are used by funders, donors and other major stakeholders. Well-designed standards incorporate, extend, or complement other standards to increase functionality and minimize effort. This paper identifies data standards that are relevant to the Common Impact Data Standard and recommends several that the Common Approach should incorporate or complement. Specifically, this paper recommends that the Common Approach take the following steps: Add data quality elements to the Common Impact Data Standard that can address fundamental issues of credibility and accuracy. Demonstrate detailed and relevant examples of the Common Impact Data Standard in formats that are used by potential users so that they can understand how it works and how it can benefit them. Ask funders and donors to adopt the use of the Common Impact Data Standard as the reporting (exchange) standard from fundees. Apply for, and secure, web standard status from schema.org and W3C. Identify key codelists and extensions that would encourage broader adoption and aggregation.
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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 | 0.191 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.034 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.046 | 0.043 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".