/academic-pipeline

External Review Protocol (Added in v2.5)

Scenario: The user submitted to a journal and received feedback from real human reviewers, bringing those comments into the pipeline.

Trigger: User says "I received reviewer comments," "reviewer comments," "revise and resubmit," etc.

Differences from Internal Review

Step 1: Intake and Structuring

1. Receive reviewer comments (supported formats):
   - Directly pasted text
   - Provide PDF/DOCX file path
   - Copy from journal system review letter

2. Parse into structured list:
   For each comment, extract:
   - Reviewer number (Reviewer 1/2/3 or R1/R2/R3)
   - Comment type: Major / Minor / Editorial / Positive
   - Core request (one-sentence summary)
   - Original text quote
   - Paper section involved

3. Produce External Review Summary:
   +----------------------------------------+
   | External Review Summary                |
   +----------------------------------------+
   | Journal: [journal name]                |
   | Decision: [R&R / Major / Minor]        |
   | Reviewers: [N]                         |
   | Total comments: [N]                    |
   |   Major: [n]  Minor: [n]  Editorial: [n]|
   +----------------------------------------+

4. Confirm parsing results with user:
   "I organized the reviewer comments into [N] items. Here is the summary — please confirm nothing was missed or misinterpreted."

Step 2: Strategic Revision Coaching (External Revision Coaching)

Unlike the Socratic coaching for internal review, external review coaching focuses more on strategic judgment:

For each Major comment, guide the user to think through:

1. Understanding layer
   "What is this reviewer's core concern? Is it about methodology, theory, or presentation?"

2. Judgment layer
   "Do you agree with this criticism?"
   - Agree -> "How do you plan to revise?"
   - Partially agree -> "Which parts do you agree with and which not? What is your basis for disagreement?"
   - Disagree -> "What is your rebuttal argument? Can you support it with literature or data?"

3. Strategy layer
   "How will you phrase this in the response letter?"
   - Accept revision: Show specifically what was changed and where
   - Partially accept: Explain the accepted parts + reasons for non-acceptance (must be persuasive)
   - Reject: Provide sufficient scholarly rationale (literature, data, methodological argumentation)

4. Risk assessment
   "If you reject this suggestion, what might the reviewer's reaction be? Is it worth the risk?"

Key principles:

• Do not default to "accept all": Real reviewer comments are not always correct — some may be based on misunderstanding or school-of-thought bias
• Encourage user to inject context: "What school of thought do you think this reviewer might come from? What context might they not be aware of?"
• User can say "just fix it for me" to skip: But when skipping strategic discussion, AI defaults to accepting all comments (conservative strategy)
• Maximum 8 rounds of dialogue, but at least 1 round per Major comment

Step 3: Revision and Response to Reviewers

Produce two documents:

1. Revised draft
   - Track all modification locations (additions/deletions/rewrites)
   - Revision content consistent with Response to Reviewers

2. Response to Reviewers letter
   Format (point-by-point response):
   +------------------------------------+
   | Reviewer [N], Comment [M]:         |
   |                                    |
   | [Original comment quote]           |
   |                                    |
   | Response:                          |
   | [Response explanation]             |
   |                                    |
   | Changes made:                      |
   | [Specific modification location    |
   |  and content]                      |
   | (or: We respectfully disagree      |
   |  because... [rationale])           |
   +------------------------------------+

Step 4: Self-Verification (Completeness Check)

Stage 3' behavior adjustments in external review mode:

1. Point-by-point comparison of External Review Summary with Response to Reviewers:
   - Does every comment have a response? (completeness)
   - Is each response consistent with actual changes? (consistency)
   - Were the places claimed as "modified" actually changed? (truthfulness)

2. New citation verification:
   - New references added during revision enter Stage 4.5 integrity verification

3. Things NOT done (different from internal review):
   - Do not reassess paper quality (that is the human reviewers' job)
   - Do not issue a new Editorial Decision
   - Do not raise new revision requests

Honest Capability Boundaries

1. AI verification does not equal human reviewer satisfaction: Stage 3' can confirm revisions are "complete and consistent," but cannot predict whether human reviewers will accept your responses. Reviewers may have unstated expectations, school-of-thought preferences, or methodological insistence
2. Unstructured comments may not parse perfectly: Some reviewers write vaguely (e.g., "the methodology needs more work"), and AI will do its best to parse but may miss implied intentions. After parsing, user confirmation is mandatory
3. AI cannot make scholarly judgments for you: "Should I accept Reviewer 2's suggestion?" is your decision. AI can provide an analytical framework, but final judgment rests with the researcher
4. Cross-cultural review convention differences: Response conventions differ across journals/academic circles (some require extreme deference, others accept direct rebuttal). AI defaults to neutral academic tone; the user can request adjustments