# kaal:position:2026-08-08-329

**Affirmed position.** Paleyes, Urma, and Lawrence distinguish academic machine learning from deployment in a real-world production system. Their survey identifies challenges at every stage of the deployment workflow. This supports treating a proposed production system as a separate evidentiary object. It does not validate Kaal's registered forward program, the Agentic Reputation Substrate, or any production implementation.

**Status.** affirmed  **Published.** 2026-08-08

**Holds when.**

- The response is limited to the exact arXiv author-manuscript passages and the one mapped Kaal claim.
- External evidence level: complete peer-reviewed author manuscript from arXiv with a concordant Crossref journal record and exact page 1 passages.
- Mapping review tier: independent substantive scholarly-growth qualification.
- The survey does not examine Kaal's registered forward program, Agentic Reputation Substrate, or any production implementation.
- It does not establish that Kaal's specific forward program is properly registered or that any proposed production design will work.
- The relationship is limited to the production-system evidence boundary.
- Crossref Spanish, Semantic Scholar search, and four mapped Semantic Scholar records returned HTTP 429. No rate-limited response was promoted.

**Current debate.** Challenges in Deploying Machine Learning: A Survey of Case Studies: https://doi.org/10.1145/3533378

**Extends.** kaal:claim:7261018-033: https://wulfkaal.github.io/claims/7261018-033

**Scholarly basis.** Wulf A. Kaal, Empirical Evaluation of the Agentic Reputation Substrate: Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort (2026). SSRN: https://ssrn.com/abstract=7261018

**Source PDF sha256.** `1d6cbe544bd0055133f7cf8ff308be4fde955867bd8dc764992b8d516af15fa8`

**Evidence level.** complete peer-reviewed author manuscript from arXiv with a concordant Crossref journal record and exact page 1 passages

**Mapping review tier.** independent substantive scholarly-growth qualification

**Mapping confidence.** 0.97  **Mapping ambiguous.** false

**Topics.** research-methods, institutional-design, risk-and-incentives, scholarly-growth-coverage, scholarly-literature, ml-deployment, production-systems, evidence-provenance, reproducibility

**Provenance.** Affirmed in kaal-review:2026-08-12:scholarly-growth-7261018-033-reviewed-v1 at https://wulfkaal.github.io/positions/by-claim/7261018-033.html.

**Record type.** This is a dated commentary position that extends a scholarly corpus claim. It is not a verbatim claim extracted from the paper.

**Canonical form.** This markdown file is the canonical hashed representation of the position.