++

2026-06-18 09:16:24 +08:00
parent 8c1feb0a31
commit 91a678ba03
1 changed files with 101 additions and 0 deletions
@@ -153,6 +153,107 @@ When in doubt, search the DB and follow the highest-priority source. See dharma-
 the person was previously poor — not necessarily true. This is about medical bankruptcy.
 → "into poverty" or "fall into poverty" (no "back")
 ## Subject-shift calques
 When the source uses an abstract/system noun as the grammatical subject (佛教,
 文明, 宗教, 文化) and the target reflexively substitutes a more concrete
 agent (Buddhist practitioners, civilization-builders, religious people, etc.),
 the English is wrong: the source is *not* talking about people, it's talking
 about the system.
 **Pattern**: 佛教都被社会大众赋予期望 → EN drifts to "Buddhist practitioners
 are looked upon with hope." Wrong subject — source is 佛教, not 佛教徒. Right:
 "Buddhism is regarded with hope by the broader society."
 **Detection**: For each translated sentence, find the *grammatical subject*
 in the English and check it matches the *grammatical subject* in the Chinese.
 If the English subject is a concrete agent and the Chinese is an abstract
 system noun, it's a subject-shift calque.
 ## Factual inconsistencies across paired descriptions
 When the same person, place, or thing is described in two different paragraphs
 (intro + later reference), check that *every* descriptor matches: title,
 affiliation, role, credentials. A translation can be factually inconsistent
 even when each individual sentence is correct in isolation.
 **Pattern**: Prof. Wei's credentials in paragraph 1: "Academician of the
 Chinese Academy of Social Sciences (CASS) and research fellow at the
 Institute of World Religions." Paragraph 5 (same person, same source
 reference): "a member of the CASS academic committee and Director of the CASS
 Buddhist Research Center." Two different titles for the same CASS affiliation.
 One is right, the other is wrong.
 **Detection**: For each named person, build a dict {name → {credential:
 sentence_refs}} and check that all credentials in the dict match. Same
 institution or title can have different renderings in different paragraphs.
 ## Calque checklist (subtle English calques of Chinese verbs)
 These are common Chinese-verb → English-verb pairs where the English word
 sounds natural in isolation but is a direct calque of the Chinese. They're
 easy to miss in a first-pass review because each one parses correctly:
 | Chinese verb | Wrong (calque) | Right (idiomatic) |
 |---|---|---|
 | 赋予 (entrust with) | "look to with hope" | "regard with hope" |
 | 得到 (obtain) | "draw forth" | "draw on" / "gain" |
 | 发挥 (bring into play) | "bring into full play" | "make the most of" |
 | 承担 (assume) | "shoulder" | "take on" |
 | 重视 | "attach importance to" | "value" / "emphasize" |
 | 体现 | "embody" / "reflect" | "show" / "demonstrate" (when abstract) |
 **Detection**: When the English uses an unusual verb that maps 1:1 to a
 Chinese word, and the Chinese word is a high-frequency academic verb (发挥,
 承担, 体现, 重视), check whether the English reads as a calque. A common
 smell: the English verb is "correct" but more formal/dramatic than the
 surrounding prose.
 ## Tonal coherence inside a parallel list
 When a list of items in a section should have parallel structure (e.g. three
 "champion X, oppose Y" items; six "developing X, strengthening Y" items),
 check that the *verb choice* is consistent across the list. Inconsistency
 within a parallel structure is a strong signal of drift.
 **Pattern**: Section V lists "First, Buddhism should serve as... Second, it
 must serve as... Third, it must serve as..." Source has 应当/要做 for all
 three. The English should match: all three "should serve as" or all three
 "must serve as." Mixing is a tell.
 **Detection**: For each parallel-list section (First/Second/Third, etc.),
 extract the verb (or other repeated slot) and verify it's identical. Drift
 inside a parallel list is one of the easiest flow issues to catch
 mechanically — just look for variance.
 ## Multi-pass review structure
 Translation review benefits from three distinct passes, run separately, each
 catching a different category of error:
 1. **Pass 1: terminology + consistency + line count** — fast, mechanical.
   Catches: 人生佛教/人间佛教 conflation, 修行/修学, 恨→resentment,
   paired inconsistencies (Buddhism vs Buddhist practitioners), missing
   content, wrong numerals.
 2. **Pass 2: mechanical/formatting** — em-dash convention, double punctuation,
   numbering mismatches, capitalisation typos, garbled text. Often skipped
   if the file "looks clean." This pass is what makes the file safe to
   publish; do it even when no content issues are obvious.
 3. **Pass 3: flow/tonal/calques** — *read the full English as a piece of
   prose*. Catch: dramatic verbs that read as calques ("draw forth,"
   "into full play," "shoulder"), intensifier drift ("profoundly important"
   × 3 in one section), consistency of "must"/"should" inside parallel
   lists, subject misattribution in calque. Often the user will prompt
   this pass with "are you sure it reads well?" or "do the words hang
   together?" Treat that prompt as a signal to re-read the whole English
   target, not just spot-check.
 Pass 3 in particular is the one that catches the *most embarrassing* errors
 — the ones where the English is grammatical and faithful but reads as
 "translationese." Don't skip it.
 ## DoT定稿 term drift
 ### 念死 → recollection of death (WRONG per DoT定稿)