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This work aims to document the constraints observed during the development of Functional Outcome Categorization (FOCP) methodology.

April 21, 2026Open Access

Functional Outcome Categorization in Human Language: A Cross-Linguistic Dataset and Benchmark for AI Alignment

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Key Points

This work aims to document the constraints observed during the development of Functional Outcome Categorization (FOCP) methodology.
Developed a typological classification for language-based outcomes.
Established preliminary frameworks for future AI alignment applications.
Identified limitations in existing classifications for further study.
Highlighted specific language classification challenges, particularly in Algonquian grammars.
Deferred full inter-rater reliability analysis to a later version.
Noted potential datasets for further investigation, such as kinship terminology.

Abstract

Scope Note: This methodology paper documents empirical and typological constraints observed during FOCP development (v0.2). The OGL kernel specification (DOI: 10.5281/zenodo.19634102) establishes prior art for the underlying triad. FOCP, DNAHS, Personal OS, and Delta Mark rubric remain in private development under a Red Hat open-core model. Known scope limitations (named, not fixed): - Algonquian animate/inanimate grammatical classification is a known case where the organizing principle's surface does not straightforwardly reduce to agent-outcome framing. Flagged for targeted treatment in a forthcoming companion paper; not addressed in present work. - Inter-rater reliability (IRR) full study is deferred to v0.3. A single-rater pilot protocol is documented in Appendix J. - Tzeltal spatial reference is flagged as future adversarial TOC expansion target. - Cross-linguistic kinship terminology noted as untapped dataset for falsifiability criterion testing; not integrated in present work. Intended for prior-art timestamping. Pre-peer-review. v0.3 planned for arXiv submission after IRR full study.

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Maxime Obongono (Sat,) studied this question.

synapsesocial.com/papers/69e71467cb99343efc98dbbe — DOI: https://doi.org/10.5281/zenodo.19647527

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Functional Outcome Categorization in Human Language: A Cross-Linguistic Dataset and Benchmark for AI Alignment

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