Reward or Penalty: Aligning Incentives of Stakeholders in Crowdsourcing
Bibliographic record
Abstract
Crowdsourcing is a promising platform, whereby massive tasks are broadcasted to a crowd of semi-skilled workers by the requester for reliable solutions. In this paper, we consider four key evaluation indices of a crowdsourcing community (i.e., quality, cost, latency, and platform improvement), and demonstrate that these indices involve the interests of the three stakeholders, namely the requester, worker, and crowdsourcing platform. Since the incentives among these three stakeholders always conflict with each other, to elevate the long-term development of the crowdsourcing community, we take the perspective of the whole crowdsourcing community, and design a crowdsourcing mechanism to align incentives of stakeholders together. Specifically, we give workers reward or penalty according to their reporting solutions instead of only nonnegative payment. Furthermore, we find a series of proper reward-penalty function pairs and compute workers personal order values, which can provide different amounts of reward and penalty according to both the workers reporting beliefs and their individual history performances, and keep the incentive of workers at the same time. The proposed mechanism can help latency control, promote quality and platform evolution of crowdsourcing community, and improve the aforementioned four key evaluation indices. Theoretical analysis and experimental results are provided to validate and evaluate the proposed mechanism, respectively.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".