Crowdsourcing for Research: Perspectives From a Delphi Panel
Bibliographic record
Abstract
Crowdsourcing, an open call for the public to collaborate and participate in problem solving, has been increasingly employed as a method in health-related research studies. Various reviews of the literature across different disciplines found crowdsourcing being used for data collection, processing, and analysis as well as tasks such as problem solving, data processing, surveillance/monitoring, and surveying. Studies on crowdsourcing tend to focus on its use of software, technology and online platforms, or its application for the purposes previously noted. There is need for further exploration to understand how best to use crowdsourcing for research, as there is limited guidance for researchers who are undertaking crowdsourcing for the purposes of scientific study. Numerous authors have identified gaps in research related to crowdsourcing, including a lack of decision aids to assist researchers using crowdsourcing, and best-practice guidelines. This exploratory study looks at crowdsourcing as a research method by understanding how and why it is being used, through application of a modified Delphi technique. It begins to articulate how crowdsourcing is applied in practice by researchers, and its alignment with existing research methods. The result is a conceptual framework for crowdsourcing, developed within traditional and existing research approaches as a first step toward its use in research.
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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 | 0.195 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.037 | 0.043 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".