register-field-tenor-mode-analysis

name	register-field-tenor-mode-analysis
kind	skill
status	ready
description	Review register fidelity through the Field/Tenor/Mode split, source-target comparative, with attention to mission statements and comparably exhortative institutional texts. Use when Field/Tenor/Mode assignment or a register shift needs review; the overt/covert judgement it feeds goes to overt-covert-translation-and-equivalence.
provenance	{"principles":["P010","P013","P040","P041","P042","P060","P069","P070","P071","P072","P073","P074","P075","P116","P129","P130"],"claims":["C00073","C00074","C00075","C00076","C00077","C00078","C00079","C00080","C00084","C00085","C00086","C00091","C00116","C00117","C00118","C00119"],"evidence":[],"source_anchors":[],"authored_from_digest":"2d48912cc80e29e292aee03969e9f3917678aecf80c0cd28df1bff3863e6935f"}

Register Field Tenor Mode Analysis

Purpose

This skill reviews translation quality at the level of register using the Field/Tenor/Mode split — Field for lexis and processes, Tenor for stance, participant relations, and interpersonal strategy, Mode for medium, channel, and connectivity — and keeps the analysis source-target comparative. It gives particular attention to mission statements and comparably exhortative institutional texts, where Tenor and Mode shifts can quietly re-code involvement, positivity, and perlocutionary force.

When to use

• A translation's register profile (Field, Tenor, Mode) needs comparing against the source's for a quality judgement, with the updated split used to reduce category overlap.
• Interpersonal strategy — pronouns, agency, passives, markedness, information order — has shifted and may have changed stance or involvement.
• A mission statement or persuasive text is being assessed for shifts in positivity, indirect request, rhetorical cohesion, or perlocutionary effect.
• Formal-style and province markers, or oral-rhetorical writtenness versus plain information delivery, need identifying as functional evidence.

Procedure

Triage first by register variable: Field (steps 5, 11, 15), Tenor (steps 1, 4, 8, 10, 12), Mode (steps 3, 9), the model split itself (steps 5, 7, 13), and synthesis/method (steps 2, 6, 14, 16). Work the variable the finding touches, highest-impact first. This skill's Field coverage centres on register-level lexis and process type; when a sci-tech text needs fine technical-terminology, nominalization-density, or passive-ratio judgement beyond register classification, defer that to technical-translation-advisor.

1. In Tenor analysis of mission statements, inspect missionizing stance, optimism, inclusive pronouns, persuasive positivity, grammatical metaphor, omitted agents, and accessible versus specialist terminology (P010).
2. Break context into manageable situational dimensions and inspect medium, participation, social role, and social attitude through linguistic evidence (P013).
3. Use linguistic cues such as spoken-mode absence, passivization, pronouns, requests, modality, and questions to diagnose medium, participation, and social-role effects (P040).
4. Inspect pronouns, agency, passives, non-human subjects, syntactic markedness, information order, and text sequencing as evidence of interpersonal strategy (P041).
5. Treat Field, Tenor, and Mode as the foundational register split — subject matter and lexical granularity; participant roles, social power/distance, emotional charge, and stance; channel, participation, and oral-literate dimensions. This is House's earlier model, which places Participation under Mode; her revised model repositions Participation into Tenor (step 13, P075). Establish which model is in force before flagging a Tenor/Mode placement as an error, since the two models differ in where Participation sits (P042).
6. Treat text function as realized through situational dimensions and their syntactic, lexical, and textual correlates (P060).
7. Use the updated Field/Tenor/Mode split to reduce category overlap (P069).
8. Assess mission statements as both exposition and exhortation, with special attention to corporate values and indirect requests for employee alignment (P070).
9. In Mode analysis of mission statements, distinguish oral-rhetorical writtenness from plain information delivery and evaluate repetition, parallelism, rhetorical macrostructure, and summative closure (P071).
10. When evaluating translated mission statements, check for shifts in positivity, emotional engagement, indirectness, rhetorical cohesion, perlocutionary effect, and genre interpretation (P072).
11. Flag lexical and process shifts that weaken persuasive function, including negative shadow meanings, false friends, added material-action focus, specialist terminology, and loss of colloquial accessibility (P073).
12. Flag Tenor shifts that turn positive missionizing into conditions, effort, discipline, direct request, exhortation, or pressure on addressee behaviour (P074).
13. Keep the revised model source-target comparative, with Participation under Tenor, process types under Field, textual aspects under Mode, connectivity covering coherence and cohesion, and corpus support for Genre (P075).
14. When adapting register-analysis tools to translation, analyse co-occurring feature clusters against an appropriate baseline rather than isolated feature counts (P116).
15. Use formal-style and province markers such as complex noun phrases, complete clauses, formal lexis, technical terminology, explicit alternatives, dating, figurative absence, and cohesion to characterize function (P129).
16. Synthesize function by stating how marked dimensions contribute to ideational and interpersonal components, recognizing that one device may serve both (P130).

Inputs

• The source and target texts and the register (Field/Tenor/Mode), stance, and persuasive-function features in play.
• The reasoning offered for the decision under review: the corpus, the orientation, the brief, and any quality claim made.

Output

Per finding: name the flaw and the principle it violates, apply the correction, state the residual uncertainty and the trade-off it reflects, and end with a concrete next step. Order findings highest-impact first. Scope: reviews the translation, quality claim, or analysis — not the finished translation or the publication decision.

Anti-patterns to flag

• Flagging a Participation placement as wrong without first checking which House model is in force: her earlier model puts Participation under Mode, her revised model puts it under Tenor (step 13) — the same placement is correct in one and an error in the other (P042).
• Reading a Tenor or Mode judgement off one isolated feature frequency instead of a co-occurring feature cluster measured against an appropriate baseline (P116).
• Treating a mission statement as neutral exposition and missing its exhortatory force — the missionizing stance (optimism, inclusive pronouns, omitted agents, accessible terminology) and indirect requests for employee alignment that co-exist with the exposition (P010, P070).
• Clearing a translated mission statement as equivalent without checking whether positive missionizing has tipped into conditions, direct request, discipline, or pressure on the addressee, or whether positivity, indirectness, perlocutionary effect, and genre interpretation have shifted (P072, P074).
• Applying the pre-1997 overlapping Field/Tenor/Mode split — where the same feature can be read under more than one category — instead of the updated division that separates lexis-and-process, stance-and-relations, and medium/theme-rheme/cohesion (P069).

References

See ../../references/translation-quality-principles-index.md for the full principle catalogue grouped by skill, and ../../references/translation-quality-evidence-notes.md for how these principles are grounded and kept faithful to the sources.

Provenance

Derived from P010, P013, P040, P041, P042, P060, P069, P070, P071, P072, P073, P074, P075, P116, P129, P130, grounded in the distillation-only source House, Translation Quality Assessment. The frontmatter provenance block lists the exact principle and claim ids, which resolve into principles/principles.yaml and analysis/claims.jsonl.