Automation Resilience and Community Benefit Framework: Methodology, Modeling, and Drafting Record
The methodology, modeling, adversarial testing, and drafting record behind the Automation Resilience and Community Benefit Framework. The model instruments themselves are deposited separately under CC0 and linked under Related works. Includes: the findings of a thirty-year scenario model tested against eleven adverse events (the workbook is held for independent review); the drafting rationale and errata record; adversarial review; a valuation methodology for benefit foregone; and a precedent analysis drawn from PEG cable franchising. Two findings are stated openly because they cut against the framework. First, the fiscal provisions are durable and the community-benefit provisions are not: six of eleven adverse events impose little or no revenue cost on the county while impairing the compute allocation by between one quarter and two thirds, so the county has limited fiscal incentive to enforce the community-benefit article. Second, the trust's monetizable base is small by construction, being half of one percent of installed capacity less the share committed to member institutions, and the operating cost model is populated entirely with placeholders. Whether a trust can fund itself from monetized idle allocation is open, and the answer is more likely no than yes. That finding will be published when cost inputs are sourced, in whichever direction it resolves. Status and limitations. Citizen-prepared work product, not legal advice. The author is not licensed to practice law and is not affiliated with, and does not write on behalf of, any university, law school, clinic, law firm, or governmental body. No attorney-client relationship arises from this deposit or from any use of it. Suggested attribution. Smith, Timothy. Automation Resilience and Community Benefit Framework: Methodology, Modeling, and Drafting Record, Release 2026.2 (2026). Independent citizen-prepared work; not affiliated with or endorsed by any university, law firm, or government body. CC BY 4.0. 10.5281/zenodo.22969877
Authors
- Timothy Smith
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-26
- DOI
- https://doi.org/10.5281/zenodo.22969877
- Primary Topic
- Artificial Intelligence in Law
- Type
- article
- Field-Weighted Citation Impact
- 0.00