Correct the language she wrote in, not the one she was practising

Every pass was English-shaped: CheckpointMessages took the text and the tone and
nothing else, so there was never a language decision to get wrong. On the live
build two pt-PT sentences drew no cards at all — Petal read the Portuguese, said
nothing about it, and filed a mechanics note about the one English line.

The rule is two decisions reading different state. What gets corrected follows
the document. What language the explanation is written in follows the writer —
the half of her pair she is not learning, from users.direction — because an
explanation is teaching, and teaching lands in the language she reads most
easily. Those coincide for every account that exists today (learnerPairs is
{"zh"}), which is a fact about the roster and not about the design, so Target
keeps them apart. It carries a third language too: the collocation gloss is
addressed to her rather than to the document, and folding it into Explain would
have quietly moved it into English on every English document.

The document verdict is a proportion, not a presence — one Portuguese quotation
must not flip an English essay. Per sentence, three-way: pair, English, or no
answer. The third value is the load-bearing one; counting the undecided as
English is exactly what would hold a journal of short Portuguese sentences in
English forever, so the Latin pairs needed an englishMarkers list curated against
pt/fr/es as carefully as latinMarkers was curated against English. Hysteresis at
70/40 because a bilingual paragraph would otherwise alternate its cards' language
every few keystrokes, and hysteresis needs a yesterday — hence the column. Plus a
corroboration floor: a ratio computed over "Não. Eu." is 100% of nothing, and a
flip rewrites every card in the document.

The verdict folds into the chunk salt beside the tone, so a document that changes
language re-opens every sentence rather than serving back cards in a language it
no longer speaks.

checkpointSystemPrompt could not simply take a language — it opens by naming the
reader an ESL learner, and appending "explain in Portuguese" hands the model two
contradictory framings. Separate constants, sharing the JSON contract below the
framing. Both carry a "never translate it into English" line, which is the
instruction the model will most want to disobey. The English prompts are
untouched byte for byte, and a golden says so out loud.

Collocation deliberately did not move: its prompt is per-language knowledge, not
framing, and "natives usually say" for Portuguese is a claim Petal cannot back.

Not deployed and not smoked against a real model. The tests drive the real router
and a real DB; what none of them prove is how Qwen behaves on a Portuguese
document, in particular whether the never-translate line holds.

Claude-Session: https://claude.ai/code/session_01GJHNvirh7Hzhc9RL3HAvz7

internal/llm/voice.go

@@ -19,9 +19,9 @@ const VoiceInterval = 20 * time.Second
 // The tone argument is accepted for a uniform pass signature but ignored: voice
 // consistency is judged against the document's own established voice, not an
 // externally-chosen register.
-func RunVoice(ctx context.Context, client LLMClient, contentText, _ string, _ Lang) ([]RawSuggestion, error) {
+func RunVoice(ctx context.Context, client LLMClient, contentText, _ string, t Target) ([]RawSuggestion, error) {
 	raw, err := client.Complete(ctx, CompletionRequest{
-		Messages:          VoiceMessages(contentText),
+		Messages:          VoiceMessages(contentText, t),
 		MaxTokens:         2048,
 		Temperature:       0.3,
 		RepetitionPenalty: 1.15,