A.6.RSIG:11 - SoTA-Echoing
This pattern is an FPF-local synthesis, not an established external term. It
carries the modern practice concern only where that concern sharpens one
description-facing recognition question: can the reader recover the right
description, its carrier or projection, its exclusions, its defining U.Episteme,
and, when §4.1’s grounded-guard condition holds, its false neighbor before
relation precision or epistemic precision-restoration work begins?
| Pattern claim carried here | Source-bearing SoTA support (post-2015) | Alignment with A.6.RSIG | Adoption status and worked-slice implication |
|---|---|---|---|
| First-contact recognition is narrower than general information architecture or documentation UX. | Jorge Arango (2018), Living in Information: Responsible Design for Digital Places; ISO/IEC/IEEE 26514:2022, Systems and software engineering - Design and development of information for users. | These sources support purposeful information places and user information shaped around what the user needs. A.6.RSIG narrows that to one encountered description: what it is for, what applies, what excludes, what carrier exposed it, and which definitionEpistemeRef identifies the defining episteme. | Adopt or narrow. Adopt the recognition and information-need concern; reject a universal UX or layout pattern. In the boundary sentence slice, the first repair is not “what does the complete Contract Bundle mean?” but “what description is this, what does it apply to, and which definitionEpistemeRef applies?” |
| Information scent helps first-contact cue economy but is not the defining episteme. | Raluca Budiu (2020), “Information Scent: How Users Decide Where to Go Next”, Nielsen Norman Group. | Information scent treats visible labels, context, and prior knowledge as imperfect estimates of source value. A.6.RSIG adopts the cue-economy insight and adds definition-episteme and exclusion discipline, with false-neighbor rejection under §4.1’s grounded-guard condition. | Adopt and add definition-episteme discipline. Adopt first-contact cue economy; reject treating familiar wording, link scent, or local projection as the defining U.Episteme. In the API slice, an endpoint label can attract attention and state deployment initiation without promising successful completion. |
| Description-recognition signatures help human and AI-assisted readers manage applicability and limitation expectations. | Amershi et al. (2019), “Guidelines for Human-AI Interaction”, CHI 2019. | Human-AI guidance emphasizes making capabilities and limits clear enough for users to calibrate trust. A.6.RSIG adapts that pressure into applies_to, excludes, definitionEpistemeRef, and admissible entry stop for human and AI-assisted readers. | Adapt. Adopt expectation management; reject making this an AI-interface pattern. In the method-note slice, the reader learns what the note can and cannot settle before using it for a decision. |
| Description-recognition cues need controlled wording without becoming synonym or alias governance. | Helen Lippell, ed. (2022), Taxonomies: Practical Approaches to Developing and Managing Vocabularies for Digital Information. | Taxonomy practice supports governed terms, validation, and maintenance for search and browse. A.6.RSIG adopts stable cue language while leaving wording repair to E.10 cues within F.19, durable naming, aliases, and collision checks to F.18, actual Bridge claims to F.9, and under-specified relation claims to A.6.P. | Adapt. Adopt controlled-lexeme discipline; reject synonym stuffing inside description-recognition signatures. The worked slices state definitionEpistemeRef, exclusions, and a false neighbor when §4.1’s grounded-guard condition holds, instead of adding more query phrases. |
| Thin echoes and projection snippets need definition-episteme anchors before a reader or retrieval system treats them as the defining episteme. | Lewis et al. (2020), “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”; Liu, Zhang, and Liang (2023), “Evaluating Verifiability in Generative Search Engines”; Gao et al. (2023), “Enabling Large Language Models to Generate Text with Citations”. | Retrieval and citation work makes source context, support, and verifiability load-bearing. Before relying on a retrieved fragment, public projection, or local example to identify a particular defining description, recover the defining U.Episteme and the projection relation when relevant. An unresolved fragment may still guide further inspection. | Adapt / narrow. Adopt source anchoring and citation-support pressure; reject a retrieval benchmark or graph-native authority. A retrieved applicability cue does not by itself settle selection semantics. |
| Description-recognition-signature adequacy is reviewable through small, case-linked checks. | Riehle, Harutyunyan, and Barcomb (2020), Pattern Discovery and Validation Using Scientific Research Methods, Technical Report CS-2020-01. | Pattern-validation practice supports explicit evidence and case adequacy. A.6.RSIG keeps that pressure lightweight: use the first-contact shape, false-neighbor rejection when §4.1’s grounded-guard condition holds, and worked slices first; add C.25 only for a composite quality-family claim, C.16.Q only for overloaded evaluative wording, and empirical evidence when the recognition claim requires it. | Adopt / lightweight. Adopt accountable validation; reject mandatory benchmark machinery for ordinary recognition repairs. |