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Lang research design

Skill brycewang-stanford/Awesome-Journal-Skills/Language-Linguistic-Society-Skills/skills/lang-research-design

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

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npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-research-design

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Use when defending the empirical design of a Language (LSA) manuscript on the terms of its subfield — elicitation and fieldwork, corpus construction, phonetic measurement, experiment, or the diachronic/typological sample. Language judges each kind of evidence by its own standards, and the design must support the theoretical claim. Defends the design; it does not run the analysis.

SKILL.md

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Research Design (lang-research-design)

Language is method-pluralist: it publishes elicited fieldwork, corpus studies, phonetic and experimental work, computational modeling, and diachronic/typological comparison, and it judges each by the standards of its own subfield. The job here is to make the design defensible to a general, possibly cross-subfield, double-anonymous reviewer — and to show the evidence actually supports the theoretical claim from lang-theory-building.

When to trigger

  • Choosing or justifying the design before data collection or analysis
  • A reader questioned the elicitation, the consultant sample, corpus coverage, measurement, or the typological sample
  • Aligning the evidence with the analysis's predictions
  • Mixed-evidence work (e.g., corpus + experiment) that must defend each component

Defend the design (by subfield)

Elicited / fieldwork data

  • Describe consultant number and background, elicitation method, and the recording/annotation workflow; distinguish elicited judgments from spontaneous/textual data.
  • Give data in numbered examples with Leipzig interlinear glossing and a source for each token; a reader must be able to see the pattern, not take it on faith.

Corpus / quantitative usage

  • Justify corpus choice, sampling frame, and coding scheme; report inter-annotator agreement for hand-coded variables; state how tokens were extracted and excluded.

Phonetic / experimental

  • Specify participants, stimuli, task, and measurement (e.g., forced alignment, formant/pitch extraction settings); pre-empt confounds; where predictions are directional, say so in advance.

Diachronic / typological

  • Make sample construction and genealogical/areal control explicit; guard against areal or bibliographic bias; keep a clear trail from primary sources to the coded generalization.

Computational / modeling

  • State what the model is a model of; separate the claim about the grammar from the properties of the architecture or training data.

Match design to claim

The single most common Language reviewer objection: the data cannot bear the generalization. Walk the chain: claim → prediction → the observation that would confirm/disconfirm it → the design's leverage on that observation. A three-language convenience sample cannot ground a universal; either narrow the claim or widen the evidence — do not overreach.

Referee-pushback patterns by subfield (the modal Language objection)

Referee writes…SubfieldThe Language-appropriate fix
"Judgments from one speaker."fieldworkadd consultants or scope the claim to the idiolect/variety
"Cherry-picked corpus tokens."corpusreport the full extraction + exclusion rule + agreement
"Confound with speech rate."phoneticscontrol or model it; show the effect survives
"Sample is areally biased."typologicalrebalance the sample or restrict the generalization

Calibration with a quick example (hedged)

Language judges each subfield by its own standard, not a single template; unlike a purely formal venue that accepts introspective judgments alone, it increasingly expects the evidence base to be visible and checkable. Illustrative: an author claims a word-order universal from four related languages; a referee flags "genealogical non-independence." The fix draws a genealogically stratified sample and restates the claim as a statistical tendency with the mechanism, so the typology can see the pattern fail as well as hold. Confirm current data expectations on the author pages and in lang-data-and-transparency.

Design pass for Language

Treat this skill as an executable review pass, not a prose hint. First lock the empirical generalization, evidence base, warrant, and theoretical payoff; then judge whether the manuscript answers the venue's real reader: linguists across subfields who value grounded analysis, transparent and checkable evidence, and careful, appropriately scoped generalizations.

  • Do the pass: lock the unit (segment / token / speaker / language), the sample, the comparison, the validity threat, and the minimum decisive evidence before recommending collection or submission.
  • Return a ledger: give claim / evidence / risk / manuscript location rows so the next agent can edit rather than rediscover the issue.
  • Sibling guard: compare against Phonology, NLLT, Journal of Semantics, Diachronica, Language Variation and Change; if a sibling owns the contribution, recommend re-routing before polishing.
  • Stop condition: do not give submission-ready advice until resources/official-source-map.md has been checked and the manuscript has one concrete fix for the largest venue-specific risk.

Anti-patterns

  • Grounding a general claim on a convenience sample that cannot support it
  • Judgments from a single consultant presented as facts about the language
  • Corpus tokens hand-picked with no stated extraction or exclusion rule
  • Phonetic effects reported without controlling obvious confounds
  • A typological sample with unacknowledged genealogical or areal dependence
  • A design that probes something adjacent to, but not, the stated prediction

Output format

【Subfield】fieldwork / corpus / phonetic-experimental / typological-diachronic / computational / mixed
【Claim it must support】from theory-building
【Design leverage】how this evidence bears on the prediction
【Key threats】consultant number, sampling, confounds, non-independence, annotation
【Evidentiary trail】data → glossed examples → claim is legible? [Y/N]
【Verdict】supports the claim / needs tightening / overreaches (fix)
【Next】lang-data-analysis

Supplementary resources

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