# kaal:claim:4755632-030

**Claim.** Mandatory crowd review and policing votes make code reviewers less likely to submit highly idiosyncratic reviews, because idiosyncratic reviewers face slashing of their reputation token scores and loss of standing in the community.

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

**Holds when.**

- applies to reviews subject to CRDAO collective policing votes

**Source quote.**

> code reviewers are less likely to engage in highly idiosyncratic reviews as they would need to fear slashing of rep token scores and loss of standing in the community.

**From.** Wulf A. Kaal, *AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning* (2024), Feedback Loops, page 40

**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, governance-design, tokenomics

**Keywords.** reputation-slashing, crowd-control, reviewer-idiosyncrasy, token-governance

**Related claims.**

- restates: https://wulfkaal.github.io/claims/4734750-024
- restates: https://wulfkaal.github.io/claims/3995709-030

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