Wider Worlds of Research for Health Equity: Public Health NGOs as Stakeholders in Open Access Ecosystems
Bibliographic record
Abstract
This article examines research uses and knowledge stakeholder politics that emerged in an exploratory study of the relevance of open access policies to a spectrum of U.S.-based public health non-governmental organizations (NGOs). This study demonstrated the clear relevance to public health NGOs of open access to peer-reviewed articles, as one form of community informatics. Though not always visible to those oriented toward academic knowledge ecosystems, public health NGOs utilize and conduct a wide range of research, both peer-reviewed and otherwise. Hence, findings indicate that public health NGOs should be more fully recognized, by researchers and policymakers in other contexts, as key stakeholders in knowledge, research, and open access ecosystems. These findings contribute to examination of community information seeking and use in the public health field, with an eye to leveraging community informatics on behalf of health equity.
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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 | 0.106 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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".