Avoiding Information Leakage in the Formation of Crowdsourcing Teams via Extended Group Multirole Assignment Considering Fairness
Bibliographic record
Abstract
The development of the Internet has led to the rapid development of a new business model called crowdsourcing. However, increasingly complex crowdsourcing tasks are difficult to be decomposed and decoupled by the performers in the actual execution. The crowdsourcing platform needs to provide a detailed task assignment method to solve this problem. At the same time, crowdsourcing tasks may involve user privacy, and protecting private information from being known by others also needs to be considered by the platform. While completing the crowdsourcing task, considering task fairness and team fairness to improve the reliability of task completion and the fairness perception of crowdsourcing members. Therefore, this article formalizes the crowdsourcing team assignment problem through Environments – Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Introducing information security constraints to construct a new sub-model (GRAINS) to solve the crowdsourcing team assignment problem. On the premise of obtaining the optimal performance assignment, the two types of fairness are discussed, providing a new decision-making scheme for the crowdsourcing platform. Through large-scale random simulation experiments, it is proved that the model can improve task and team fairness while ensuring the overall performance of the task, and quantitatively analyze the partial performance for fairness sacrificed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".