Technology-Mediated Citizen Science Participation: A Motivational Model
Bibliographic record
Abstract
We propose and test a framework of the antecedents of contribution in two technology-mediated citizen science projects, with different degrees of task granularity. Comparing earlier findings on the motivations of volunteers in a web-based image analysis project (high granularity), with new findings on the motivations of volunteers in a volunteer computing project (low granularity), we found that participation task granularity is correlated with motivation levels. Further, we found that collective and intrinsic motives are the most salient motivational factors, whereas reward motives are less important for volunteers. Intrinsic, norm-oriented and reputation-seeking motives were most strongly associated with participation intentions, which were, in turn, associated with participation. Finally, comparing the relationship between motives and participation among the two volunteer populations, we found that active-participation volunteers are characterized by significantly stronger association between collective motives and contribution intention, whereas passive-participation volunteers are characterized by significantly stronger association between identification with the community and contribution intention. Implications for research and practice are discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".