Expanding opportunities to maximise research recruitment and data collection using digital tools
Bibliographic record
Abstract
While digital tools are often recommended for researchers, there is a lack of evidence around effective social media strategies among researchers to optimise participant recruitment and data collection. However, an ‘add Facebook and stir’ approach could create extra burden for participants or foil researchers’ efforts. Participant recruitment using digital tools requires a high degree of knowledge and strategy to be effective. Data collection with digital tools offers new avenues for social researchers to engage with different types of data. Online photos, databases, and social media posts offer opportunities to use data analysis techniques like thematic analysis or photovoice with broad data sets. There are specific ethical issues with digital recruitment and data collection, including relationship boundaries and informed consent. The insights in this article can support researchers to widen participation in their studies, sample from broader geographic areas and data sets, access different kinds of data, and increase the feasibility of social 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 | 0.268 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.025 |
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".