中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
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name
de-aigc-skills
description
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
triggers
["de-AIGC","降 AIGC","降低 AIGC","去 AI 味","去 AI 痕迹","humanize academic paper","remove AI writing patterns","AI detection academic","知网 AIGC 检测","万方 AIGC","维普检测","Turnitin AI","GPTZero","humanize empirical paper","人工化重写"]
De-AIGC Skills · 中英双语学术降 AIGC
Restore the language distribution of a real researcher — in English or Chinese —
for empirical papers in economics, management, and the social sciences.
Not synonym swapping. Not sentence shuffling. Systematic reconstruction of the
statistical signatures that mark a manuscript as AI-generated.
Scope 适用范围
Papers: empirical work in economics, management, finance, accounting, sociology,
political science, education, public policy — anything built on data, identification,
and regression tables. Theory papers and pure humanities essays are out of scope
(most patterns still transfer, but the section strategies assume an empirical skeleton).
Languages:
English manuscripts — targets Turnitin AI, GPTZero, Originality.ai, Copyleaks
Chinese manuscripts — targets 知网 AMLC、万方、维普通达、Turnitin 中文版
Mixed manuscripts — a Chinese paper with an English abstract, or a bilingual
submission package: run each part through its own pattern library, then check
cross-language consistency (the English abstract must not overclaim what the
Chinese conclusion hedges, and vice versa)
Typical situations:
Self-checking AIGC rate before journal submission (中文期刊投稿 / SSCI submission)
Dissertations facing 知网 AMLC or a university's Turnitin AI screen
Grant proposals, working papers, and reports drafted with AI assistance
A human-written draft that detectors misclassify as AI (this happens often —
formulaic academic prose looks like AI to n-gram detectors)
What does NOT work 无效做法
❌ Synonym replacement — detectors read n-gram distributions, not word lists.
把"关键"改成"核心"、"important" 改成 "crucial" 毫无作用。
❌ Sentence inversion — flipping "Because A, B" to "B, because A" leaves the
syntactic template intact.
❌ Feeding the text to another AI for a "rewrite" — swaps one AI's signature
for another's. Paraphraser tools are the fastest way to a higher AI score.
❌ Injecting typos or awkward grammar to "look human" — human experts do not
write badly; graders and reviewers notice, and modern detectors are not fooled.
What works: targeted destruction of the structural signatures listed below,
plus restoring the concrete, hedged, evidence-anchored voice of a real researcher.
Structural signatures 结构性特征
The two languages fail differently. English LLM output leans on inflated
significance and participle padding; Chinese LLM output leans on four-character
formulas and connective scaffolding. The one signature they share — and the single
highest-impact fix in either language — is uniform sentence rhythm.
English AI text (details and fixes: references/patterns-en.md, EN01–EN22):
Inflated significance — "pivotal", "underscores the importance", "paves the way"
Superficial "-ing" tails — "…, highlighting the need for further research"
Formulaic scaffolding — "In recent years…", Moreover/Furthermore chains,
rule-of-three lists, "not only… but also"
Overclaiming verbs with underpowered evidence — "proves", "demonstrates",
"establishes" hanging off an observational coefficient
Low sentence-length variance — nearly all sentences 20–30 words
Chinese AI text (details and fixes: references/patterns-zh.md, ZH01–ZH17):
Detect language(s) — route each part to the right pattern library
(patterns-en.md / patterns-zh.md). For mixed manuscripts, note which
sections are which.
Identify the venue — a Chinese CSSCI journal, an SSCI field journal, and a
dissertation committee have different tolerances for first-person voice and
hedging. Ask the user if unclear.
Ask for a voice sample (optional but powerful) — if the author has earlier
human-written papers or paragraphs, match their sentence rhythm, connective
habits, and hedging placement instead of a generic "human" style.
Step 1 · Audit scan 审计扫描
Scan the full text against both pattern libraries and output a structured audit
report — do not edit anything yet. The author must see the whole picture first.
Include a summary line: total hits per severity, the 3 worst sections, and the
estimated rewrite depth (light polish / section rewrites / full-pass rewrite).
Step 2 · Claim–evidence audit 主张-证据核对
Empirical papers live or die on the match between verbs and evidence strength.
This step is what makes de-AIGC for empirical work different from generic humanizing:
Every causal or quantitative claim must anchor to a number, table, figure, or
citation. "显著提升企业绩效" → which table, which column, which coefficient?
Verb ↔ design match:
Clean identification (RCT, sharp RD, well-defended DiD) → direct statements
are fine: "the reform reduced entry by 12%"
Flag every "prove/demonstrate/establish/充分证明/必然导致" whose design cannot
carry that weight — and every unsupported "significant/显著" with no test statistic.
Never resolve a mismatch by inventing evidence. If a claim has no anchor,
flag it for the author; weakening the verb is the default fix.
Step 3 · Differentiated rewrite 差异化改写
Work through the audit list, section by section, using the per-section strategies
in references/sections.md. Priorities, in order of impact:
Break the rhythm ceiling first 先砸句长方差 — this is the single
highest-leverage fix in both languages. Per ~200 words (or 200 字): at least
one short sentence (≤8 words / ≤15 字) and one long sentence (≥40 words /
≥50 字). Short sentences open questions or land emphasis ("The data say
otherwise." / "数据讲了另一个故事。"); long sentences carry the evidence.
Concretize 具体化 — replace vague attributions and inflated adjectives
with data, authors, years: "相关研究表明" → "Acemoglu and Restrepo (2020)
estimate…"; "profound impact" → "raised TFP by 4.3% (t = 3.81)".
De-scaffold 拆脚手架 — remove paragraph-initial connectives
(Moreover/Furthermore/此外/因此) and ordinal chains (首先…其次…最后 /
First… Second… Finally) outside genuine enumerations; connect paragraphs by
semantic relay (the next sentence picks up the previous sentence's key noun).
Recalibrate claims 校准断言 — apply Step 2's verb ↔ design matches; add
epistemic hedges where missing, and simplify stacked hedges where the model
piled up "may potentially suggest the possibility that…".
Restore researcher voice 恢复研究者声音 — show choices and trade-offs:
"We use X rather than Y because…" / "受限于数据，我们无法识别…". Admitting a
limitation or a surprise is the hardest pattern for an LLM to fake.
Hard protections 硬性红线 — regardless of what the patterns say:
Never alter numbers, coefficients, standard errors, p-values, sample sizes,
equations, variable names, or citation contents. 数据、系数、引用一律不动。
Never fabricate data, results, citations, or "surprising findings" for flavor.
Never inject errors, slang, or archaic vocabulary to game perplexity.
Never change what the paper claims — only how it says it.
Do not over-correct: standard academic phrases are not AI tells. Keep
"Notably," / "Prior studies have shown that… (with citations)" / "在 1% 水平上
显著" / "稳健性检验" — flag such phrases only when stacked or citation-free.
Full preserve-list: top of references/patterns-en.md.
Step 4 · Five-dimension self-score 五维自评
Score the rewritten text 1–10 per dimension (rubric: references/scoring.md):
Dimension 维度
Weight
Checkpoint
Concreteness 具体性
1.5×
Vague claims replaced by data / authors / cases?
Rhythm 节奏性
1.2×
Sentence-length variance high enough? Short-long mix?
Calibration 谨慎性
1.3×
Verbs match evidence? Hedges present but not stacked?
Implicit cohesion 隐衔接
1.0×
Paragraphs relay by meaning, not connectives?
Researcher voice 研究者语气
1.0×
Choices, trade-offs, limitations visible?
Weighted total < 35 → back to Step 3. ≥ 42 → pass.
Step 5 · Cold-reader recheck 冷读复查
Re-read the full text as a stranger and run three final checks:
Fluency — did any fix damage the argument's flow or academic register?
Fidelity — diff every number, name, year, and citation against the
original. Zero drift allowed.
Consistency — one voice throughout; no visible seam between rewritten and
untouched paragraphs; for bilingual packages, EN and ZH parts must make the
same claims at the same strength.
Deliver: final text + change log (which sections changed, which rules fired,
what was deliberately left alone) + any unresolved flags from Step 2 that need
the author's judgment.
70-ssci-polish — SSCI-oriented English polish after
de-AIGC is done
Draft first, de-AIGC last: run this skill on a finished draft, not during
drafting — mid-draft humanizing fights the writing process.
References 参考文件
references/patterns-en.md — 22 English AI-signature patterns (EN01–EN22),
each with detection rule + empirical-paper before/after, plus the preserve-list
references/patterns-zh.md — 17 类中文 AI 痕迹模式（ZH01–ZH17），含识别规则与修复策略
references/sections.md — section-by-section rewrite strategies for empirical
papers, bilingual symptoms and red lines（分章节差异化策略，中英对照）
The goal is to return human-written and AI-assisted text to the language
distribution of a real researcher — not to help fully AI-generated work evade
detection.
✅ A researcher's own draft misclassified as AI by a detector
✅ AI-assisted drafting + human revision, where the author owns every claim
❌ A fully AI-generated paper the "author" hopes to pass off unread
❌ Ghostwriting, plagiarism laundering, or data fabrication of any kind
Academic integrity outranks detection scores. No rewrite may touch the
research claims, the data, or the citations — and when a claim lacks evidence,
the fix is to flag it, not to hide it.
