Developing a scalable framework for partnerships between health agencies and the Wikimedia ecosystem
Bibliographic record
Abstract
In this era of information overload and misinformation, it is a challenge to rapidly translate evidence-based health information to the public. Viewership data following the Ebola crisis and during the COVID-19 pandemic reveals that a significant number of readers located health guidance through Wikipedia and related projects, including its media repository Wikimedia Commons and structured data complement, Wikidata. In 2013, Wikipedia’s medical content consisted of more than 155,000 articles and 1 billion bytes of text in over 255 languages, and the number of views during that year surpassed 4 billion, making it the most viewed medical resource worldwide. The research idea discussed in this paper aims to increase and expedite health institutions' global reach to the general public, by developing a specific strategy to maximize the availability of focused content into Wikimedia’s public digital knowledge archives. It was conceptualized from the experiences of leading health organizations such as Cochrane, the World Health Organization (WHO), Cancer Research UK, National Network of Libraries of Medicine, and CDC's National Institute for Occupational Safety and Health (NIOSH). Each has customized strategies to integrate content in Wikipedia and evaluate responses. The research idea is to develop an interactive guide on the Wikipedia and Wikidata platforms to support health agencies, health professionals and communicators in quickly distributing key messages during crisis situations. The guide aims to cover basic features of Wikipedia, including translation into multiple languages; automated metrics reporting; sharing non-text media; anticipating offline reuse of Wikipedia content in apps or virtual assistants such as Apple's Siri or Google Assistant; using Wikidata to collect, curate, and share data; and a discussion of other flagship projects from major health organizations. In the first phase, we propose the development of a curriculum for the guide using information from prior case studies. In the second phase, the guide would be tested on select health-related topics as new case studies. In its third phase, the guide would be finalized and disseminated.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".