# Selection

`kaal:entity:selection`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `selection`. 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

3 claims across 2 works, 2018 to 2026.

**2018**

- [3128900-027](https://wulfkaal.github.io/claims/3128900-027) [mechanism/argued] -- Because requesters select workers by reputation score, workers acquire an incentive to keep their scores high by performing tasks with high accuracy and efficiency.
  > Requesters can select workers based on their reputation score, giving workers an incentive to keep the reputation scores high by performing micro tasks with high accuracy and efficiency.
  Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900

**2026**

- [7598518-015](https://wulfkaal.github.io/claims/7598518-015) [condition/reported] -- Feng et al. (2025) show that verifier-selected synthetic data can prevent collapse, and the paper concedes the core of that result: verifier selection is an established remedy, and the oracle ceiling and the Gaussian setting are established.
  > 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
- [7598518-019](https://wulfkaal.github.io/claims/7598518-019) [mechanism/argued] -- Filters that select on surprise or entropy, not validity, work where they work because they see what a validity filter is forbidden to see, and against that literature a null here is a literature rather than a caveat.
  > 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/selection.md | sha256sum

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