Public Involvement and Engagement in Big Data Research: Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research. OBJECTIVE: This review aims to synthesize the evidence on public involvement and engagement in big data research. METHODS: This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review. RESULTS: A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research. CONCLUSIONS: This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-050167.
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.
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 | 0.055 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it