Bibliographic record
Abstract
This netnographic study investigates how and why people engage with citizen science initiatives and share insights from them in the context of the COVID-19 pandemic. Specifically, this research focuses on biohacking, a form of citizen science in which individuals conduct innovative but controversial self-experiments. In a context of ideological, behavioral, and emotional tensions, biohackers seek to do what they consider to be “the right thing” for themselves and others. Some biohackers believed that governmental “solutions” for the pandemic were not “correct” or “the best” and shared scientifically unproven protocols to develop, for example, homemade vaccines. However, in many cases, biohackers may unintentionally create harm while intending to do good by sharing such “solutions.” In this vein, this research shows that sharing is not always caring, as biohacking related to COVID-19 exemplifies. Although sharing is a form of prosocial behavior, it has different motivations that may invert its epistemic prosocial orientation to an antisocial one. This orientation results in new challenges, as well as strengthening old challenges, for policy makers facing public crises, such as pandemics. The prescriptions for policy makers offered in this article aim to help reduce such an impact on governmental efforts to tackle collective crises.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".