Citizen science or scientific citizenship? Disentangling the uses of public engagement rhetoric in national research initiatives
Bibliographic record
Abstract
BACKGROUND: The language of "participant-driven research," "crowdsourcing" and "citizen science" is increasingly being used to encourage the public to become involved in research ventures as both subjects and scientists. Originally, these labels were invoked by volunteer research efforts propelled by amateurs outside of traditional research institutions and aimed at appealing to those looking for more "democratic," "patient-centric," or "lay" alternatives to the professional science establishment. As mainstream translational biomedical research requires increasingly larger participant pools, however, corporate, academic and governmental research programs are embracing this populist rhetoric to encourage wider public participation. DISCUSSION: We examine the ethical and social implications of this recruitment strategy. We begin by surveying examples of "citizen science" outside of biomedicine, as paradigmatic of the aspirations this democratizing rhetoric was originally meant to embody. Next, we discuss the ways these aspirations become articulated in the biomedical context, with a view to drawing out the multiple and potentially conflicting meanings of "public engagement" when citizens are also the subjects of the science. We then illustrate two uses of public engagement rhetoric to gain public support for national biomedical research efforts: its post-hoc use in the "care.data" project of the National Health Service in England, and its proactive uses in the "Precision Medicine Initiative" of the United States White House. These examples will serve as the basis for a normative analysis, discussing the potential ethical and social ramifications of this rhetoric. We pay particular attention to the implications of government strategies that cultivate the idea that members of the public have a civic duty to participate in government-sponsored research initiatives. We argue that such initiatives should draw from policy frameworks that support normative analysis of the role of citizenry. And, we conclude it is imperative to make visible and clear the full spectrum of meanings of "citizen science," the contexts in which it is used, and its demands with respect to participation, engagement, and governance.
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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 | 0.119 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.116 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".