# Code

`kaal:entity:code`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `code`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 3 works, 2018 to 2026.

**2018**

- [3227933-029](https://wulfkaal.github.io/claims/3227933-029) [design/asserted] -- The design premise of the DAO was that because people do not always follow rules even when the rules are well designed, it is better to use computer code to manage an organization.
  > The DAO intended to automate governance and was based on the idea that since "people" don't always follow the rules (even if the rules are well-designed), it is better to use computer code to manage an organization.
  Mark Fenwick, Wulf A. Kaal, Erik P.M. Vermeulen, Why 'Blockchain' Will Disrupt Corporate Organizations (2018). SSRN: https://ssrn.com/abstract=3227933

**2026**

- [7596939-023](https://wulfkaal.github.io/claims/7596939-023) [empirical/reported] -- The experiment was pre-registered and time-stamped before any registered episode ran, with design, prompts and analysis code frozen in advance.
  > Should intelligent agents have legal rights? This Article sets that question aside and asks a smaller one that matters more in practice. An intelligent agent that earns a reputation record has something to lose.
  Wulf A. Kaal, Should an Intelligent Agent Have Legal Rights? (2026). SSRN: https://ssrn.com/abstract=7596939
- [7598518-004](https://wulfkaal.github.io/claims/7598518-004) [condition/stated] *(failure mode)* -- Model engines served as blind auditors of the design, the code and the text, and they are not independent peer review.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518
- [7598518-011](https://wulfkaal.github.io/claims/7598518-011) [mechanism/stated] -- The analysis refuses to unseal unless the design hash is accepted, the tree is clean and matches its commit byte for byte, and the code differs from the audited commits only in a named allowlist.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/code.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.