Public engagement on global health challenges
Bibliographic record
Abstract
BACKGROUND: Experience with public engagement activities regarding the risks and benefits of science and technology (S&T) is growing, especially in the industrialized world. However, public engagement in the developing world regarding S&T risks and benefits to explore health issues has not been widely explored. METHODS: This paper gives an overview about public engagement and related concepts, with a particular focus on challenges and benefits in the developing world. We then describe an Internet-based platform, which seeks to both inform and engage youth and the broader public on global water issues and their health impacts. Finally, we outline a possible course for future action to scale up this and similar online public engagement platforms. RESULTS: The benefits of public engagement include creating an informed citizenry, generating new ideas from the public, increasing the chances of research being adopted, increasing public trust, and answering ethical research questions. Public engagement also fosters global communication, enables shared experiences and methodology, standardizes strategy, and generates global viewpoints. This is especially pertinent to the developing world, as it encourages previously marginalized populations to participate on a global stage. One of the core issues at stake in public engagement is global governance of science and technology. Also, beyond benefiting society at large, public engagement in science offers benefits to the scientific enterprise itself. CONCLUSION: Successful public engagement with developing world stakeholders will be a critical part of implementing new services and technologies. Interactive engagement platforms, such as the Internet, have the potential to unite people globally around relevant health issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.026 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".