Paying Crowd Workers for Collaborative Work
Why this work is in the frame
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Bibliographic record
Abstract
Collaborative crowdsourcing tasks allow crowd workers to solve problems that they could not handle alone, but worker motivation in these tasks is not well understood. In this paper, we study how to motivate groups of workers by paying them equitably. To this end, we characterize existing collaborative tasks based on the types of information available to crowd workers. Then, we apply concepts from equity theory to show how fair payments relate to worker motivation, and we propose two theoretically grounded classes of fair payments. Finally, we run two experiments using an audio transcription task on Amazon Mechanical Turk to understand how workers perceive these payments. Our results show that workers recognize fair and unfair payment divisions, but are biased toward payments that reward them more. Additionally, our data suggests that fair payments could lead to a small increase in worker effort. These results inform the design of future collaborative crowdsourcing tasks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it