Field: Social Sciences

A field overlay: it supplements, not replaces, the general agent_docs. Read it before discipline-specific writing. It encodes the conventions and reviewer expectations of social-science venues so the agent does not write lab-science prose into a psychology submission, and respects APA style, the confirmatory/exploratory line, and the field's measurement standards.

Activate it in CLAUDE.project.md → Field overlay.

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Venues & where content goes

• Main text vs supplement / OSM: the confirmatory analyses that test the preregistered hypotheses go in the main text; robustness checks, full item wording, additional models, and exploratory analyses go to Online Supplementary Materials — clearly labeled.
• A data-availability / open-practices statement and, where applicable, the preregistration link are expected; reviewers check for them.

Structure (APA-style IMRaD)

A typical layout: Introduction → Method → Results → Discussion, with a structured Method.

• Method subsections: Participants (and recruitment/eligibility), Materials/Measures, Procedure, Design, and an analysis plan. Enough detail to replicate (agent_docs/reproducibility.md).
• Confirmatory vs exploratory is a structural divide, not a footnote. If the study was preregistered, the Results report the registered tests first, as confirmatory; everything else is labeled exploratory in its own subsection.
• Results past tense; Discussion present for interpretation (agent_docs/academic-style.md). Keep observed effects apart from their interpretation and from limits on generalization.

APA 7 style (the house style for much of the field)

• In-text citation: author–date — (Smith & Jones, 2020) or "Smith and Jones (2020) found…"; three+ authors use "et al." from first cite. Lock natbib/apacite or a CSL APA-7 style; do not hand-format (CLAUDE.md → Model vs Code).
• Reference list: hanging indent, alphabetical, DOIs as URLs. A CSL processor produces this — the model does not retype references.
• Numbers & stats: italic test statistics (t, F, r, p), report df; numbers below 10 spelled out in prose except with units/stats.
• Bias-free language (APA ch. 5): person-first or identity-first per community preference; specific, respectful descriptors for age, disability, gender, race/ethnicity, sexual orientation; avoid "subjects" for humans (use "participants"). This is a substantive APA requirement, not optional polish — but it never changes a reported finding (a Protected Claim if the meaning shifts, CLAUDE.md).

Preregistration & the replication crisis

The field's central methodological reform; reviewers are primed for it.

• Preregister the hypotheses, design, and analysis plan (OSF / AsPredicted) before data collection. If preregistered, cite the registration and flag every deviation in Method — a silent deviation is worse than none (agent_docs/reproducibility.md).
• Confirmatory ≠ exploratory. A preregistered prediction tested as planned is confirmatory; anything decided after seeing data is exploratory and labeled so. The honest verb for exploratory results is "suggests" / "is consistent with."
• No HARKing (Hypothesizing After Results are Known) — do not present a post-hoc finding as an a-priori hypothesis (agent_docs/statistics.md).
• Registered Reports (peer-reviewed before data collection, in-principle acceptance) are the strongest format against publication bias — note if the manuscript is one.

Construct validity & measurement

A finding is only as good as its operationalization — the heart of social-science rigor.

• Operationalization: state how each abstract construct (e.g. "implicit prejudice", "social capital", "wellbeing") was measured, and cite the validated instrument. A construct is not its label.
• Reliability: report internal consistency (Cronbach's α or McDonald's ω) for multi-item scales; test–retest where relevant. State the value and the scale it applies to — "α = .82 for the 6-item anxiety scale," not a bare "reliable."
• Validity: address content, convergent/discriminant, and criterion validity where the claim depends on it. A high α does not establish that the scale measures the intended construct.
• Measurement invariance when comparing groups — a scale must function equivalently across groups before mean differences are interpretable.

Study design

• Sampling: describe the population, recruitment, eligibility, and the sampling frame; characterize the sample (and its limits — convenience/online panels constrain generalization). State who was excluded and why.
• Power analysis: an a-priori power analysis justifying N for the target effect size (e.g. via G*Power), stated with the assumed effect, α, and power. Underpowered designs are a leading reviewer objection.
• IRB / ethics & consent: approval (with protocol number) and informed consent stated; for deception or vulnerable populations, the safeguards. The kit does not invent an approval ID — [VALUE — verify] if unknown (CLAUDE.md → Source-Grounded Writing).
• Attrition & manipulation checks: report dropout and exclusions by condition; for experiments, report the manipulation check that shows the intervention did what it claimed. Differential attrition across conditions undermines randomization.

Qualitative methods (held to their own rigor standard)

Qualitative work is not "soft" — it has explicit quality criteria reviewers apply.

• Coding & analysis: name the approach (thematic analysis, grounded theory, IPA, framework analysis) and describe how codes were developed (inductive/deductive) and applied.
• Inter-rater reliability for coded categorical data: Cohen's κ (two coders) or Fleiss' κ / Krippendorff's α (more), reported with the value; or, for interpretive traditions, describe consensus-coding instead — and say which.
• Reflexivity: state the researcher's position and how it may shape interpretation; this is expected, not optional, in much qualitative reporting.
• Saturation: justify sample size by data/thematic saturation rather than power.
• Trustworthiness (Lincoln & Guba): credibility, transferability, dependability, confirmability — the qualitative analogues of validity/reliability; address the ones the claims rest on. Use an audit trail and member checking where appropriate.

Statistics (see agent_docs/statistics.md)

• Effect sizes always, with confidence intervals — Cohen's d, r, η², odds ratios — not bare significance. APA expects effect sizes reported alongside tests.
• CIs over bare p. Report the estimate and its interval; "p < .05" alone hides magnitude and precision. The reportable triplet: estimate · CI · test (with N).
• No p-hacking: disclose all measured variables, conditions, and exclusions; report the analysis you preregistered and label deviations. Correct for multiple comparisons or justify why not.
• Causal-inference caution: observational / cross-sectional / correlational data do not license "causes", "leads to", "the effect of." Default to "is associated with", "predicts", "is related to." Causal language requires a design that earns it (experiment, valid instrument, RDD, well-specified diff-in-diff) — and the identifying assumption stated (agent_docs/statistics.md).
• Mediation/moderation: a mediation model on cross-sectional data tests a causal pathway it cannot establish; report it as the conditional association it is, and note that temporal precedence is unobserved.
• Model specification: state covariates and why; do not present the one specification that reached significance out of many (agent_docs/statistics.md — disclose the specification curve or the robustness set).

Citations & notation

• Style: APA-7 author–date via apacite/CSL; cite the published version, not a working paper, once one exists. A theoretical construct's origin is cited to its source, not to a textbook restatement (CLAUDE.md → Source-Grounded Writing).
• Common knowledge for the subfield needs no citation; a specific empirical estimate (an effect size, a prevalence) always does.
• Brace-protect acronyms in BibTeX titles so casing survives — {IRT}, {SEM}, {COVID-19}.

Calibration in this field (overclaim → calibrated)

The verb/scope ladder of agent_docs/academic-style.md, applied to social science:

Pattern: scope to the sample, report effect size + CI, and use associational verbs unless the design earns causation.

Data & code sharing

• Deposit de-identified data and analysis code on OSF / a DOI-bearing archive (Zenodo, Dryad); cite full materials (stimuli, survey instruments, code) so the study is reproducible. Open-data/open-materials badges follow.
• Human-data privacy: de-identify before sharing; restricted access for sensitive data with the reason stated, not as a default dodge. The deposit DOI is real — [VALUE — verify] until minted, never fabricated (block-fabrication.sh).

Typical reviewer concerns (pre-empt them)

MANUSCRIPT_MAP.md additions for social-science papers

Add to your map's Claims that need extra care:

• Observational/correlational findings are associational unless the design licenses causation — state the identifying assumption.
• A reliable scale (high α) is not necessarily a valid measure of the named construct — keep reliability and validity claims separate.
• Results from a convenience/online sample do not generalize to the broader population unless shown.
• An exploratory result is labeled exploratory; it does not become confirmatory because it was predicted post hoc.

Add to Data & reproducibility: preregistration ID/link (or [VALUE — verify]) and whether it is a Registered Report, IRB approval and consent statement, the validated instruments with reliability, and the OSF/DOI deposit for de-identified data + materials + code.