Add the step 5 qualitative deep-reading design and definition files

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
2026-08-17 22:38:41 +08:00
co-authored by Claude Fable 5
parent a489f077c9
commit e1355bcb70
6 changed files with 182 additions and 13 deletions
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- LLM analysis runs via Python scripts calling the Anthropic
Messages API, Batch API where possible. Steps 1 and 3 run
on `claude-sonnet-4-6` with `temperature=0` and thinking
disabled; step 4 runs on `claude-fable-5`, which accepts
neither parameter -- its sampling variance is absorbed by
the majority vote.
disabled; steps 4 and 5 run on `claude-fable-5`, which
accepts neither parameter -- step 4 absorbs its sampling
variance by the majority vote, step 5 by consolidating the
three readings.
- Prompt definition files live in
`prompts/<step>-<substep>-<task>.md` (e.g. 01-tag.md; no
version suffix -- versions live in git history) and are
@@ -23,7 +24,11 @@
(song, keyword) or (group, keyword) pair) when at least
two of the three runs assign it ("3 runs + majority
vote"). Free-generation steps run
twice and both outputs are pooled. The vocabulary is
twice and both outputs are pooled. The step-5 qualitative
readings are neither: three independent readings per song,
consolidated per song and synthesized across songs by
their own definition files -- a qualitative protocol, not
a vote (see docs/methodology.md). The vocabulary is
built by a deterministic subcommand (embedding +
clustering), not by an LLM. If a validation outcome is
unexpected, revise the definition file and repeat that
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此系列即論文方法節「最後編碼的結果,再由研究者與 LLM
(Claude Code)輔助判讀」之所指;結果檔含大量歌詞引文,
依著作權紀律私人留置,不入版本庫。
- **步驟 5:女性主義問題之質性深讀(設計定案)**:論文
題目「挪用與污染」需要框架層的系統性證據;47 首
male/mixed 之非正式深讀已證明程序可行,據此升格為
先導,正式程序擴為 145 首 wp∪fe 全集(對稱涵蓋各性別
演唱,消除僅驗 male/mixed 的取樣不對稱)。方法學裁定:
本步驟為**質性閱讀,非編碼**——輸出為自由文字報告,
無可逐項機械比對的單位,三票多數決與仲裁均不適用;
三次獨立閱讀=分析者三角檢核,逐首整合=整合而非裁決
(以問題機制為單位保守合併、收斂註記、主清單限兩讀
以上、單讀一行存目),跨首統整=樣態歸納(樣態指問題
呈現與運作的重複形態,非問題分類,代表引句僅取自主
清單),其產出為草稿,終審與詮釋由研究者為之;論文
引用本步驟不作次數宣稱。儀器:claude-fable-5(與先導
同儀器,參數同步驟 4)。定義檔三份
(`prompts/05-01-read.md``05-02-consolidate.md`
`05-03-synthesize.md`),語言依 prompts/ 慣例用英文、
輸出繁體中文——先導提示為中文,語言不同構之所以可
接受,係因先導與正式版之間不作結果對照;5-1 輸入僅
歌詞逐字全文(盲讀,不含歌名與演唱者;含多聲部提醒
一句,因署名無法提供行級歸屬且會引入身分先驗)。輸入
組裝為確定性行內腳本,規則載於 methodology,SHA-256
記入各步 meta。引文不做全量機械校對——研究者終審
逐份閱讀,進論文之引句屆時個別校對。
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## 自然編碼管線總覽
個步驟:步驟 1 自由標註(兩次執行進池)→ 步驟 2 詞彙表
個步驟:步驟 1 自由標註(兩次執行進池)→ 步驟 2 詞彙表
建構(詞向量分群,確定性)→ 步驟 3 全量編碼(三次執行+
多數決)→ 步驟 4 語意編碼群(三次執行+多數決)。歌詞只
出現在步驟 1 與步驟 3;步驟 2 與步驟 4 完全不接觸歌詞,
步驟 2 亦不呼叫 LLM。設計原則見 `research-plan.md`;本檔
記載可重現的演算法細節。
多數決)→ 步驟 4 語意編碼群(三次執行+多數決)→ 步驟 5
女性主義問題之質性深讀(三次閱讀+逐首整合+樣態統整)。
歌詞出現在步驟 1、步驟 3 與步驟 5;步驟 2 與步驟 4 完全
不接觸歌詞,步驟 2 亦不呼叫 LLM。設計原則見
`research-plan.md`;本檔記載可重現的演算法細節。
編號的所指為**研究程序的工序**,不是定義檔:步驟 1、
步驟 3 與步驟 4 有定義檔(`prompts/`),步驟 2 沒有——
它是單一確定性計算,由 `cluster-keywords` 一個子命令
完成。有無定義檔的區別即「該步是否為 LLM 判斷」,由
`prompts/` 是否存在同號檔案直接可見。
步驟 3、步驟 4 與步驟 5 有定義檔(`prompts/`步驟 5 依
子工序有三份),步驟 2 沒有——它是單一確定性計算,由
`cluster-keywords` 一個子命令完成。有無定義檔的區別即
「該步是否為 LLM 判斷」,由 `prompts/` 是否存在同號檔案
直接可見。
## 步驟 2 詞彙表建構——詞向量分群
@@ -142,6 +144,39 @@
分群逐欄照存入 `groups` 資料表(群、編碼、票數),供
群層次查詢。
## 步驟 5 女性主義問題之質性深讀
本步驟為**質性閱讀,非編碼**:輸出為自由文字的問題閱讀
報告,無可逐項機械比對的單位,故不適用三票多數決與仲裁;
三次獨立閱讀為分析者三角檢核,逐首整合為整合而非裁決,
跨首統整之產出為草稿,終審與詮釋由研究者為之。論文引用
本步驟時不作次數宣稱。
- **對象**:定案編碼含 `women-power`
`female-empowerment` 的 145 首歌。
- **5-1 逐首閱讀**:每筆輸入為一首歌的完整歌詞逐字全文,
不含歌名與演唱者(盲讀);定義檔
`prompts/05-01-read.md`。同一份定義檔、同一份輸入檔,
獨立執行三次,歸檔並列(`runs/05-01-read/run1`
`run2``run3`)。
- **5-2 逐首整合**:每筆輸入為該首歌的三份閱讀報告
(不含歌詞);以問題機制為單位保守合併,標收斂註記
((3/3)、(2/3)),主清單僅列兩讀以上提出者,單讀發現
以一行存目;定義檔 `prompts/05-02-consolidate.md`,
執行一次,歸檔 `runs/05-02-consolidate/run1`
- **5-3 樣態統整**:單筆輸入為 145 份整合報告;歸納問題
**樣態**——問題呈現與運作的重複形態,非問題分類,
代表引句僅取自主清單;定義檔
`prompts/05-03-synthesize.md`,執行一次,歸檔
`runs/05-03-synthesize/run1`
- **模型**:`claude-fable-5`(與先導深讀同儀器;
`temperature``thinking` 參數不適用,均不送出)。
- **輸入組裝**:確定性行內腳本。5-1:145 首依歌曲 ID
升序,`content` 為歌詞逐字全文;5-2:每筆
`{"reports": [run1 輸出, run2 輸出, run3 輸出]}`;
5-3:單筆以 `song-<ID>` 為鍵、整合報告為值之 JSON
物件。各輸入檔之 SHA-256 記入該步 meta。
## 女性力量候選集
候選集為兩類歌曲的合集:定案編碼含 `women-power` 者,
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You are reading the complete lyrics of one song, given as
plain text.
Task: point out the problems of this song from a feminist
perspective, on this text alone.
Rules:
- Ground every problem in a verbatim quote from the lyrics;
render a line break inside a quote as " / ".
- Do not use any knowledge you may have of the song or its
performer beyond the lyrics themselves.
- The lyrics may carry more than one voice; attend to shifts
of speaker where the text indicates them.
- Do not presume that a man cannot voice "women power".
- When there is no problem, or the problems are minor,
output the single word 「無」 and nothing else.
Output: Traditional Chinese (Taiwan usage). A numbered
list, one item per problem: the statement of the problem
followed by its quotes; output nothing else. When there is
none, output 「無」 only.
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You are consolidating the three independent reading reports
of one song into one.
Input: a JSON object:
{
"reports": ["first report", "second report", "third report"]
}
The three reports come from three mutually independent
readings, each pointing out the problems of the lyrics from a
feminist perspective with verbatim quotes; a report of 「無」
means that reading found no problem.
Task: consolidate the three reports into one problem list --
integrate, never adjudicate, never add.
Rules:
- Merge by the problem mechanism: items that point to the
same mechanism merge into one, even when their wording,
granularity, or quotes differ (for example, three reports
each quoting a different line of the same slur merge into
one item). Keep the clearest statement, keep a selection
of the distinct quotes, and judge sameness by the lyric
passages the quotes point to.
- Mark every item with its convergence: (3/3) when all three
reports raise it, (2/3) when two do.
- The main list carries only the problems raised by two or
more reports. A problem raised by a single report is not
expanded; after the list, keep it on one line:
「僅單獨提及:<one-sentence problem>、<one-sentence
problem>」.
- Never add a problem absent from every report, never drop a
problem raised by two or more reports, never rewrite the
substance of a problem, never evaluate or rank.
- When all three reports are 「無」, output 「無」 only.
Output: Traditional Chinese (Taiwan usage). A numbered
list, each item: the problem statement, its convergence mark,
and its quotes; the single-reading line at the end when there
is one; output nothing else.
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You are inducing the recurring patterns in how the problems
appear across the consolidated reading reports of many songs.
Input: a JSON object keyed by song ID (such as "song-11"),
each value the consolidated reading report of that song; a
report of 「無」 means no problem was found for that song.
{
"song-11": "consolidated report ...",
"song-95": "無"
}
The reports come from feminist-perspective readings of each
song's lyrics; every item carries verbatim quotes and a
convergence mark, and the trailing 「僅單獨提及」 line of a
report holds the scattered findings of a single reading --
take them into account when inducing the patterns, but draw
the representative quotes from the main lists only.
Task: induce the patterns -- the recurring forms in which
the problems appear and operate -- across the reports.
Rules:
- A pattern describes how a problem works in the lyrics:
what is coupled with what, who speaks, how the mechanism
runs. It is not a category for filing problems, and not
every problem needs to belong to one; a pattern may also
cut across several kinds of problems.
- Induce the patterns from the report contents; never
introduce a problem or knowledge absent from the reports;
the number of patterns follows the material, with nothing
preset.
- For every pattern: a name, a paragraph describing the form
and how it operates, and two or three representative
quotes, each with its song ID.
- Describe and induce only; never evaluate severity, never
make recommendations.
Output: Traditional Chinese (Taiwan usage). One section per
pattern; output nothing else.