Accent Dial

name: accent-dial description: >- Pre-terminal accent stage: dial a controllable amount of the author's Serbian-L1 accent into an article by accepting a ranked fraction of EN→Serbian→EN round-trip translation edits. One cached round-trip per article (gemma4:31b-cloud), paragraphs aligned 1:1, mechanically gated (citations, numbers, locks, length), ranked by Serbian-ness (calques, then restructuring depth); --dial 0..1 applies the top fraction deterministically with a full edit log. Generative source, deterministic application: the run is not done until the applied paragraphs pass an entailment review. Triggers: accent dial, serbian accent, round-trip translation, dial the accent, more serbian, less serbian, translation laundering, L2 accent stage.

Accent Dial (pre-terminal stage)

The EN→SR→EN round-trip is the only measured source of the author's Serbian-L1 accent: the regular published articles carry ZERO calques and sit below the native exemplars on the L2 composite (paper-stash writing-voice/l2-markers.yaml, v1.2), while the gemma round-trip of Strategy Theatre measured +0.726 — and simultaneously produced the strongest Pangram move ever recorded on that article (0.708 → 0.150 fraction_ai, all 27 citations intact). But a whole-text round-trip is all-or-nothing: locked spans get paraphrased, quoted specimens drift from their blockquotes, and confident mistranslations ride along.

This stage makes the accent a dial instead. The round-trip is a CANDIDATE GENERATOR, not a transformation: every paragraph pair is gated mechanically, the survivors are ranked by how much Serbian they carry, and --dial p applies the best-ranked fraction. Application is deterministic and prefix-monotone (edits applied at a lower dial stay applied at every higher one), every candidate lands in the edit log with its gate verdict and score, and unapplied paragraphs stay byte-identical to the input.

Pipeline position (GH-57 ordering; a humanize stage since GH-208)

This is a stage of the humanize chain — its Phase 4, after tighten-style and before inject-vernacular (terminal). The candidates come from a generative model, so the stage must precede the deterministic terminal stage and the caller's read-only review phase. Locked spans never enter the candidate pool (the gate skips any paragraph carrying lock markers), but locks are excised and spliced by the calling pipeline as usual — the gate is a backstop, not the mechanism. It also runs standalone when the author only wants the dial.

Usage

python3 <skill>/scripts/accent_dial.py --article draft.md --dial 0.4

• First run generates and caches <stem>.roundtrip.txt via Ollama (~150 chunk calls on a 5k-word article; reruns at other dial values are free). --roundtrip points at an existing cache.
• Output: <stem>.dial<p>.md + <out>.log.json (per-candidate gate verdict, score, applied flag — the survival-analysis surface).
• Fluency dial (GH-188). --fluency {fresh,settled,native} (or --fluency-years N, mapped <=8 / <=22 / else) gives the return leg an immersion persona, dialing the accent between the mechanical round trip's total-beginner sound and polished-away. Measured on the way in: a bare years number in the prompt is a null — four levels produced identical fluent output — so years only select a described feature band (fronted adverbs and dropped articles at fresh, faint formality at settled, idiomatic at native). Absent, the blind return leg is byte-identical to the calibration.
• Two dials since GH-186. --model-return (env ACCENT_DIAL_MODEL_RETURN) splits the legs: the 2026-08-21 A/B located the accent effect on the return leg, so the productive pairing is a strong outbound translator (fidelity into the pivot) with the weak return one (where the accent is born) — e.g. --model cohere:command-a-03-2025 --model-return gemma4:31b-cloud. --language swaps the pivot (default serbian); any other pivot produces its own accent flavor but sits outside the calque gate's calibration — score() then ranks by restructuring distance alone, and the run says so on stderr. Per-language marker banks (l2-markers.yaml is the canonical home) are the eventual fix.
• --model (or ACCENT_DIAL_MODEL) overrides the translator, and since GH-184 the script rides match-voice's shared transport, so cohere:command-a-03-2025 routes with key handling and retries for free. The default stays gemma4:31b-cloud on the 2026-08-21 A/B: the stronger gpt-oss return leg polishes the accent away (L2 composite -0.009 vs gemma's +0.726) and scores worse on Pangram (0.247 vs 0.150) — the same risk applies to any stronger translator, Cohere included, so the pipeline-wide Cohere default deliberately does not reach this skill.

Calibration (Strategy Theatre payload, 2026-08-21)

Two saturation points, both useful: the L2 composite saturates by ~0.25 (ranking front-loads every calque-bearing paragraph), and Pangram saturates by ~0.5 — half the edits buy the whole detector effect while the other half of the article stays byte-identical to the author-gated text. Default working range: 0.3–0.5. Above 0.5 you pay review surface for nothing measurable.

Sentence grain (default since GH-73)

The GH-175 author gate rejected paragraph grain: dial 0.4 produced walls of fully-translated paragraphs beside untouched ones, and the whole read as ESL. Sentence grain dials the intensive margin instead — candidates are 1:1-aligned sentences (monotone DP alignment; split/merged sentences never pair, and the sentence-level length gate kills half-translations), globally ranked, applied under a per-paragraph cap (--max-per-para, default 2). The accent disperses: one lightly foreign sentence per paragraph, nothing fully foreign. A quote gate rejects any candidate whose double-quoted spans are not verbatim — mechanizing the failure class that cost 5 of 9 review reverts at paragraph grain.

Calibration (strategy-theatre payload, cached gemma round-trip, baseline 0.708 AI / full round-trip 0.150):

The shapes differ: sentence grain beats paragraph grain at low dial (0.374 vs 0.464) and plateaus near 0.4 — dispersed swaps blend inside detector windows, so it never reaches the concentrated grain's floor. Choose by objective: register smoothness and author tolerance → sentence (start 0.3); maximum laundering on structurally clean text where the author accepts paragraph walls → paragraph.

The review gate (mandatory)

Mechanical gates pass what semantic review rejects — the 8/8 match-voice lesson. After applying, entailment-review every applied paragraph against its original (the edit log lists them): meaning preserved, no confident mistranslation ("the pods sentence" → "a sentence about floors" passed every mechanical gate), quoted phrases still match what they quote. Reject by reverting the paragraph in place (the log records the original index; unapplied paragraphs are untouched) — never by asking a model to repair the final text. Then re-measure: paper-stash writing-voice/measure-l2.py --text <out> for the accent, --pangram-style scan only at the gate (scans cost credits; the dial curve above is the planning surface).

Generalization (three pre-pipeline essays, 2026-08-21)

Dial 0.4 on essays that predate the voice program, all baseline 1.000 AI (prose-only payloads):

The split has a measured mechanism: round-trip launders diction, not discourse structure. The translation preserves — and sometimes amplifies — the structural tells (junior antithesis 8→18 through the round-trip, failed 9→14; tricolons, anaphoric lists, and opening monotony pass through nearly unchanged), so an essay saturated with structural signal scans 1.000 even fully translated. Strategy Theatre responded because its structure was already pipeline-cleaned.

Consequences:

• accent-dial composes AFTER structural repair, never instead of it. Run the filter-tells structural pass first; dial the accent into structurally clean text.
• gemma manufactures antithesis (an AI tell inject-vernacular targets), so a structural recheck of applied paragraphs is part of the review gate.
• The calque list does not transfer across articles: all three fresh round-trips produced zero hits on the strategy-theatre-seeded list, so ranking degenerated to restructuring depth. Until an article-independent L2 signal exists, treat the ranking as laundering-depth-first on new material.

Known limits

• The ranking's accent signal is the calque list mirrored from l2-markers.yaml — article-specific in practice (see Generalization); grow the canonical bank first, then mirror here.
• Saturation shape (accent by 0.25, Pangram by 0.5) measured on strategy-theatre only, and only meaningful where the round-trip moves the score at all — check the essay responds before choosing a dial.
• Paragraph-level grain: a paragraph is swapped whole. Sentence-level grain is a follow-up if review-gate rejections cluster in otherwise good paragraphs.