# kaal:claim:4755632-040

**Claim.** Paying workers a share of incoming compensation pro rata to their reputation scores makes rigor self-enforcing, because workers who do not engage with the required care lose their spot in the reputation rankings and thereby lose their share of the job fee distribution.

**Type.** mechanism  **Support.** argued

**Holds when.**

- applies to the ALE Platform fee distribution design

**Source quote.**

> they sacrifice their spot in the rankings of reputation scores which affects their participation in the job fee distribution, which is paid out pro rata to the respective reputation scores.

**From.** Wulf A. Kaal, *AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning* (2024), Gamification of Micro Task Work, page 50

**Cite as.** Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632

**Verify.** sha256 of source PDF `972ccebf0c06ac1767a9e443bb95942b7670e806a63c25ee817c368a64c8eca8` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Learning%20-%20Decentralized%20Governance%20to%20Optimize%20Human%20Output%20Datasets%20for%20AI%20Learning.pdf

**Topics.** reputation, risk-and-incentives

**Keywords.** pro-rata-rewards, reputation-scores, incentive-design, micro-task-quality

**Related claims.**

- generalizes: https://wulfkaal.github.io/claims/4855607-029

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.