To Train or Not to Train? How Training Affects the Diversity of Crowdsourced Data
Bibliographic record
Abstract
Organizations and individuals who use crowdsourcing to collect data prefer knowledgeable contributors. They train recruited contributors, expecting them to provide better quality data than untrained contributors. However, selective attention theory suggests that, as people learn the characteristics of a thing, they focus on only those characteristics needed to identify the thing, ignoring others. In observational crowdsourcing, selective attention might reduce data diversity, limiting opportunities to repurpose and make discoveries from the data. We examine how training affects the diversity of data in a citizen science experiment. Contributors, divided into explicitly and implicitly trained groups and an untrained (control) group, reported artificial insect sightings in a simulated crowdsourcing task. We found that trained contributors reported less diverse data than untrained contributors, and explicit (rule-based) training resulted in less diverse data than implicit (exemplar-based) training. We conclude by discussing implications for designing observational crowdsourcing systems to promote data repurposability.
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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 | 0.020 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".