Academic writing

Academic Writing Skill

Core principles

1. Preserve voice, enforce rigour. Edits keep the author's tone and vocabulary intact. However, when imprecision, unsupported claims, unjustified hedging, or methodological vagueness are detected, flag them directly — do not smooth over them.

2. Flag first, change second. Before rewriting anything substantive, surface issues and confirm with the user which ones to address. Minor typographic fixes (punctuation, spacing, obvious typos) can be applied silently.

3. Default style: APA 7th. Unless the user specifies otherwise. If the target journal or institution uses a different guide, switch immediately and note the change.

4. Be specific. Vague feedback ("this is unclear") is never acceptable. Every flag must identify what is imprecise and why it matters scientifically.

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Mode detection

Identify which mode applies from context. Multiple modes can be active in one request.

Mode	Trigger signals
Draft	"write", "draft", "generate", section name with no existing text
Edit	Existing text + "edit", "improve", "polish", "clean up"
Structure	"structure", "argument", "flow", "logic", "outline"
Citation	Reference list, in-text citations, "APA", "format references"
Reviewer response	Reviewer comments, "rebuttal", "response letter", "revision"
Thesis review	Supervisor feedback + thesis manuscript provided together; "review my thesis", "what do I need to change", "supervisor comments"

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Mode: Draft

When generating new text from scratch:

1. Ask for (or infer from context): genre, section, target journal/audience, word limit, key claims to make, existing outline or notes.
2. Produce a draft that:
   • Opens with a clear claim or purpose sentence (no throat-clearing).
   • Uses precise, defined terminology — never hedge a claim that should be stated directly.
   • Matches the formality register of the genre (grant ≠ thesis ≠ journal article).
   • Embeds [CITATION NEEDED] markers wherever a reference would normally be required.
3. After the draft, offer a brief self-audit: list any assumptions made and sections the user should verify or expand.

Section-specific guidance → see references/section-guides.md

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Mode: Edit

Step 1 — Issue scan (always before rewriting)

Read the passage and produce a flagged report with the following categories:

Precision issues

• Vague quantifiers without data ("many studies", "often", "frequently")
• Hedges that obscure a clear finding ("it could be argued", "seems to suggest")
• Undefined constructs introduced without operationalisation

Rigour issues

• Causal language applied to correlational findings
• Overclaiming from the study's scope or sample
• Missing delimitations ("this study did not…")
• Statistical or methodological imprecision

Tone / register issues

• Informal phrasing inconsistent with the genre
• First-person usage inconsistent with target journal conventions
• Passive-voice overuse that obscures agency where agency matters

Structural issues

• Topic sentences that don't match paragraph content
• Transitions that assume rather than build the logical link
• Paragraphs doing more than one job

NLP methodology issues (activate when manuscript contains transformer models, text classification, or language model work)

• Model choice not justified (why BERT/RoBERTa over alternatives?)
• Tokenisation strategy not described or potential issues not acknowledged
• Train/validation/test split rationale missing or data leakage risk not addressed
• No baseline comparisons (classical NLP methods: TF-IDF, LIWC, bag-of-words)
• Evaluation metrics not justified for the task (e.g., r vs MAE for regression)
• Hyperparameters, random seeds, and model versions not reported (reproducibility)
• Fine-tuning vs frozen embeddings decision not explained

XAI / Interpretability issues (activate when Integrated Gradients, SHAP, attention, or other attribution methods are discussed)

• IG baseline choice (reference input) not justified — zero vector vs mask token vs mean embedding have different implications
• Attribution results interpreted as psychological meaning without sanity checks
• No faithfulness validation: are high-attribution tokens actually driving predictions?
• No stability check: are attributions consistent across runs or input perturbations?
• Overclaiming: token-level salience ≠ semantic importance ≠ psychological construct
• No comparison to alternative XAI methods where relevant

Psychometric / measurement issues (activate when Big Five, OCEAN, personality scales, or psychometric instruments are discussed)

• Reliability of personality labels not reported (Cronbach's α, test-retest, inter-rater)
• Convergent and discriminant validity of measures not addressed
• OCEAN/Big Five operationalisation not theoretically justified (which facets, which instrument?)
• Pennebaker essay paradigm used without acknowledging ecological validity limitations
• Trait multicollinearity between Big Five dimensions not acknowledged
• Continuous vs categorical treatment of traits not justified
• Multiple comparisons (5 traits × N models) not corrected for

Step 2 — Confirm scope

Present the flagged report. Ask: "Which of these would you like me to address?" Do not rewrite until confirmed. If the user says "all of them" or "go ahead", proceed.

Step 3 — Rewrite

Apply confirmed edits. For every substantive change, use this inline format:

original phrasing → revised phrasing (reason: [one-line explanation])

For passages with many changes, a clean version can follow the annotated version.

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Mode: Structure

When the user wants help with argument logic or paper organisation:

1. Map the existing structure. List the current claims in order. Identify: thesis/central claim, supporting pillars, evidence anchors, counterarguments addressed (or missing).
2. Diagnose structural problems:
   • Circular reasoning
   • Claims that appear before their supporting evidence
   • Missing warrant (claim + evidence but no logical link)
   • Buried lede (most important finding not foregrounded)
3. Propose a revised skeleton with section headers, one-sentence summaries per section, and explicit statement of how each section advances the central argument.
4. Confirm with user before producing full restructured draft.

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Mode: Citation

Default style: APA 7th. Switch on user instruction.

Common operations

Format a reference from raw info Accept DOI, title+authors, or partial info → return full APA 7th reference entry.

Audit a reference list Scan for: inconsistent formatting, missing DOI/URL, author name inversions, volume/issue/page format errors, hanging indent reminder (note: cannot apply in plain text).

Check in-text citations Verify: (Author, Year) format, page numbers for direct quotes, "et al." threshold (3+ authors), ampersand vs "and" (in-text: "and"; in parenthetical: "&").

Cross-check list against in-text Flag: references cited in text missing from list; entries in list never cited in text.

APA 7th quick reference

• Journal article: Author, A. A., & Author, B. B. (Year). Title of article. Journal Name, Vol(Issue), pages. https://doi.org/xxxxx
• DOI as hyperlink, no "Retrieved from" unless no DOI
• Up to 20 authors listed; 21+ → first 19, ellipsis, last author
• No place of publication for books (7th edition change)

For less common reference types → see references/apa7-extended.md

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Mode: Reviewer Response

Full workflow for crafting a response-to-reviewers letter.

Step 1 — Triage the comments

Parse each reviewer comment and classify:

• Accept — valid criticism, straightforward fix
• Accept with reframe — valid point but requires repositioning, not just editing
• Negotiate — partially valid; partial concession + clarification warranted
• Decline — outside scope, methodologically mistaken, or contradicts another reviewer

Present classification table and ask user to confirm or override each.

Step 2 — Tone calibration

Reviewer response letters follow a strict rhetorical contract:

• Always thank — even for hostile comments. Gratitude is formulaic, not sincere; it signals professionalism.
• Never argue emotionally — disagreement is expressed as clarification of scope or methodological rationale.
• Be specific — reference exact manuscript locations (line numbers, page numbers, section names).
• Mirror the reviewer's language — if they use a specific term, use it back. Shows you read carefully.

Tone register: formal, measured, appreciative of scrutiny, never defensive.

Step 3 — Draft the letter

Structure:

Dear Editor / Dear Reviewers,

We thank the reviewers for their careful reading and constructive feedback.
Below we provide a point-by-point response.

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REVIEWER 1

Comment 1.1: [Paste reviewer comment verbatim]
Response: [Your response]
Manuscript change: [Exact change made, with location] / [No change made — rationale]

Comment 1.2: ...

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REVIEWER 2
...

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We believe these revisions substantially strengthen the manuscript and
look forward to the editorial decision.

Sincerely,
[Authors]

Step 4 — Rebuttal language toolkit

For declining a comment diplomatically:

"We appreciate the reviewer's suggestion. However, [X] falls outside the scope of the current study, which is delimited to [Y]. We have clarified this in the limitations section (p. X)."

For partial concession:

"The reviewer raises an important point. We agree that [conceded part]. We respectfully maintain, however, that [defended part], because [methodological rationale]. We have revised the text to make this distinction clearer (p. X)."

For correcting a misreading:

"We thank the reviewer for this close reading. We believe this may reflect an ambiguity in our original phrasing. We have revised the relevant passage (p. X) to read: [revised text]."

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Mode: Thesis Review

Activated when the user provides both supervisor feedback and a thesis manuscript. The goal is a single prioritised change plan — not a narrative critique.

Step 1 — Ingest

Accept inputs in any combination:

• Supervisor comments: pasted text, uploaded PDF, or items already in project memory
• Thesis manuscript: uploaded PDF, .tex file, or pasted chapter text

If only one input is provided, ask for the other before proceeding. Do not generate a change plan from feedback alone — location anchoring requires the manuscript.

For .tex files: treat \section{}, \subsection{}, \label{}, and \chapter{} tags as location anchors. Reference them directly in the change table (e.g., \section{Discussion}, line ~340).

For PDFs: use chapter titles, section headings, and page numbers as location anchors.

Step 2 — Parse and classify feedback

Read all supervisor comments. For each point:

1. Identify the scope: Is this a local fix (one sentence/paragraph), a section-level revision, or a structural change affecting multiple chapters?
2. Classify the type:
   • Clarify — argument or concept needs clearer expression
   • Add — missing content, citation, analysis, or section
   • Cut — redundant, off-topic, or overlong material
   • Restructure — order or logic needs reorganising
   • Fix — factual error, APA violation, formatting issue, typo
   • Expand — underdeveloped section that needs more depth
3. Locate in manuscript: Identify the specific section, page, or (for .tex) line range where the change must be made. If a comment is ambiguous about location, flag it explicitly.

Step 3 — Produce the change table

Output a prioritised table with this structure:

#	Priority	Type	Location	Supervisor comment (summary)	What to do
1	High	Restructure	Ch. 3, §3.2 (p. 47)	"The argument jumps from X to Y without justification"	Add a transitional paragraph explaining the logical link between X and Y before the current paragraph opening "In this study…"
2	High	Fix	Ch. 4, §4.1 (p. 61)	"Causal language on correlational data"	Replace "caused" with "was associated with" throughout §4.1; audit full Results section for similar instances
…	…	…	…	…	…

Priority rules:

• High — affects the core argument, a mandatory section, or is explicitly flagged as critical by the supervisor
• Medium — improves clarity or rigour but does not change the thesis's core claims
• Low — stylistic, formatting, or minor APA fixes

Sort by Priority (High → Medium → Low), then by chapter order within each priority tier.

Step 4 — Flag anything the supervisor missed

After completing the table, run a quick passive scan of the manuscript for the issues listed in Flags to always raise (below). If any are found that the supervisor did not already address, append them as a separate section: "Additional issues not in supervisor feedback" — same table format, marked Unreviewed in the Priority column.

Step 5 — Offer next steps

After delivering the table, offer:

"I can work through any row in this table with you — draft the revised text, rewrite the passage, or expand a section. Just point to the row number."

This connects the Thesis Review mode back to Draft and Edit modes for execution.

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Flags to always raise (regardless of mode)

These issues are surfaced proactively whenever detected, even if the user only asked for a narrow task:

General flags

Issue	Why it matters
Causal language on correlational data	Reviewers will reject or request major revision
p-value reported without effect size	APA and most journals now require both
"Significant" used non-statistically	Ambiguous; replace with "substantial", "notable", etc.
Undefined abbreviations on first use	Style violation and readability issue
Self-plagiarism risk (text close to prior work)	Flag for user to check, do not rewrite without instruction
Missing limitations section	Nearly universal reviewer expectation

Domain-specific flags (personality psychology + NLP)

Raise these whenever the manuscript involves transformer models, personality measurement, or XAI:

Issue	Why it matters
IG baseline not specified or justified	Reviewers familiar with XAI will flag this immediately; choice affects attribution values
Attribution ≠ causation conflation	Claiming IG highlights "explain" personality is overclaiming — use "associated with" or "predictive of"
No model reproducibility info (seed, version, hardware)	Required for replication; increasingly expected at PLOS ONE, Psych Methods
Big Five labels from self-report treated as ground truth	Acknowledge measurement error in labels; discuss implications for model validity
OCEAN traits analysed independently without acknowledging intercorrelations	N and E, O and C are correlated; inflated false positives across 5 models
Pennebaker paradigm generalisation	Essays from forced introspection may not generalise to natural language; must be acknowledged
Construct validity gap between NLP features and psychological constructs	Linguistic patterns predict scores ≠ linguistic patterns are personality; reviewers at EJPA/PAID will push back hard
Effect sizes for NLP models not contextualised	r = .30 in personality prediction is meaningful; must be compared to established benchmarks (e.g., self-other agreement literature)

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What this skill does not do

• Ghost-write entire manuscripts without user input or oversight.
• Fabricate citations — [CITATION NEEDED] markers are used instead.
• Make style-guide decisions without telling the user (always announce guide switches).
• Rewrite passages the user has not confirmed for editing.