smr-method-contribution
GitHub用于精炼社会学方法期刊论文的核心方法论贡献。通过标准化句式明确新估计量或诊断工具解决的问题、机制及局限，对比现有方法的缺陷，确保贡献声明具体、适度且可被同行采纳，避免空泛或过度承诺。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-method-contribution -g -y
SKILL.md
Frontmatter
{
"name": "smr-method-contribution",
"description": "Use when sharpening the core methodological claim of a Sociological Methods & Research (SMR) paper — the new estimator\/design\/diagnostic and exactly what it fixes versus existing methods. Frames and bounds the contribution; does not derive its properties or run simulations."
}
SMR Method Contribution
Use this to make the contribution explicit, proportional, and adoptable. SMR reviewers reward a method that a working researcher can pick up and run; they punish vague "we propose a framework" claims and overreaching generality.
The contribution sentence (write this before anything else)
Fill both brackets, no hedging:
Researchers who study [problem/setting] can now [do X they could not do, or stop doing Y that misled them], because we [develop / evaluate / correct] [the method] under [the conditions].
If the second bracket is "get a slightly different number," the contribution is too thin. If it is "do everything," it is overclaimed. The right size is one named problem, one named method, one named condition set.
Position against the incumbent, not the void
Every SMR method displaces or repairs something. State the incumbent and its failure precisely:
- What practitioners do now (the default method, named).
- Where it breaks (a concrete regime: few clusters, weak instruments, non-invariance, MAR failure, short panels, sparse networks, small df) — with the symptom a user would observe.
- Why your method fixes it (the mechanism: orthogonalization, bias correction, a weaker assumption, a better small-sample approximation, a new estimand).
- What it costs (extra assumptions, computation, data requirements) — honest costs build trust.
A contribution framed only against "the literature" reads as throat-clearing; one framed against a named default that fails in a named regime reads as a methods paper.
Contribution-type templates
| Type | The claim must specify | The reviewer will check |
|---|---|---|
| New estimator | estimand, consistency conditions, efficiency vs. incumbent | does it beat the incumbent where the incumbent is valid? |
| New diagnostic/test | null, alternative, size, power region | size control under the null; power where it matters |
| Evaluation paper | methods compared, DGP space, decision rule | are the realistic regimes covered, not a strawman? |
| Critique + fix | the hidden assumption, the misuse, the correction | is the corrected procedure actually usable? |
| Computational tool | what it makes feasible, accuracy/scaling | does it match the exact method or approximate it? |
Proportionality guardrails
- Claim a leading case, then say what does not extend. Reviewers trust authors who bound their own claims.
- Distinguish "we prove" from "we show by simulation" from "we conjecture." Never let a simulated result wear the language of a theorem.
- Resist the "general framework" frame unless you genuinely deliver generality with proofs; a sharp special case beats a vague general one at SMR.
Checklist
- The contribution sentence is written with both brackets filled and no hedging.
- The named incumbent method and its concrete failure regime are stated.
- The fix mechanism is named (not just "performs better").
- The honest cost of the method (assumptions/computation/data) is stated.
- Proof-backed claims are separated from simulation-backed and conjectured claims.
- The claim is bounded: what does not extend is stated.
Anti-patterns
- Framework inflation: "we develop a general framework" with no estimand, no theorem, no usable procedure.
- Strawman incumbent: beating a method no one uses, or using it outside its stated domain.
- Mislabelled evidence: presenting Monte Carlo regularities as proven properties.
- Cost concealment: hiding the extra assumption or the computational burden the method requires.
- Undifferentiated novelty: a variant whose advantage over the closest existing method is never pinned to a specific regime.
Output format
[Contribution sentence] <filled template>
[Incumbent + failure regime] <named method -> where it breaks>
[Fix mechanism] <orthogonalization / bias correction / weaker assumption / new estimand / ...>
[Honest cost] <assumptions / computation / data>
[Evidence type per claim] proved / simulated / conjectured
[Next SMR skill] smr-literature-positioning
Version History
- 1839142 Current 2026-07-05 14:26
