chi-reproducibility
GitHub用于增强ACM CHI论文的研究透明度，涵盖协议、分析脚本和数据处理。确保方法通过ADR-Method筛选，支持人类受试者约束下的数据共享与可复现性，提供定量与定性透明度的具体实践指南。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-reproducibility -g -y
SKILL.md
Frontmatter
{
"name": "chi-reproducibility",
"description": "Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work."
}
CHI Reproducibility
Reproducibility at CHI is not "same script, same numbers." Human-subjects research reproduces at the level of protocol and analysis: could a competent lab run your study again, and could a skeptic re-derive your findings from your materials? CHI's screening now names "research transparency" explicitly inside the ADR-Method assisted desk-reject ground, so opacity is a pre-review rejection risk. The working principle for data: as open as consent allows, as documented as possible where it does not.
Three layers, three different obligations
| Layer | What must be true | Typical artifacts |
|---|---|---|
| Protocol | Another lab could run the study | Task descriptions, scripts read to participants, stimuli, apparatus specs, recruitment text, screening criteria, compensation |
| Analysis | A skeptic could re-derive results from your data | Analysis code, codebook + coding decisions, exclusion rules, model specifications, software versions |
| Data | Shared where consent permits; described honestly where not | De-identified quantitative data, aggregate tables, transcript excerpts, or a documented reason why not |
The protocol layer is the cheapest and the most neglected: your consent scripts,
questionnaires, and interview guides already exist — publishing them in the
supplement costs an afternoon and answers half of the methods questions reviewers
would otherwise raise (chi-supplementary).
Quantitative transparency
- Ship the analysis pipeline: raw-to-clean transformation, exclusions with counts and reasons, and the exact statistical models. Pin versions (R/Python, packages).
- Preregistration (OSF, AsPredicted) is increasingly normal for confirmatory CHI
studies; during review, link an anonymized view only — a named OSF project is
an anonymization violation (
chi-submission). - Report every measured variable somewhere, including ones that showed nothing; selective reporting discovered later damages more than a null result ever would.
- Randomization, counterbalancing assignments, and seed-equivalents (trial-order generation) belong in the materials, not in folklore.
Qualitative transparency
Qualitative work cannot ship a replication button; it can ship an audit trail:
- The interview guide or diary prompts, verbatim, including probes.
- The codebook where the method uses one — codes, definitions, example excerpts — or, for reflexive approaches, a documented account of how themes developed.
- Analysis-process notes: who coded, how disagreements were handled, memo samples.
- Transcript excerpts beyond those quoted in the paper, where consent allows — reviewers increasingly distrust papers whose only visible data is ten quotes.
Data sharing under human-subjects constraints
Never promise what consent cannot deliver. The honest ladder, top rung you can reach:
- Full de-identified dataset in a persistent repository (OSF, institutional archive).
- Partial release: quantitative measures public, recordings withheld.
- Aggregate data plus instruments and codebook.
- No data, documented reason (consent scope, re-identification risk, community agreements — common and respected in work with vulnerable populations), plus a contact path for mediated access if any exists.
For AI-infused systems add: model name and version/date, prompts and parameters, and cached model outputs from the study window, because the hosted model your participants used will not exist next year. A CHI study of "the assistant" without a pinned version is unreplicable by construction.
The availability statement
State per artifact class what is available, where, and why not where not:
Availability. Study protocol, interview guide, questionnaires, and the full
codebook: <repository DOI>. De-identified quantitative data and analysis
scripts (R 4.4, renv lockfile): same repository. Audio recordings and raw
transcripts are not shared, per the consent agreement; extended anonymized
excerpts appear in the supplement. LLM condition: <model+version>, prompts
and all cached outputs included.
During review this statement appears with anonymized links; at camera-ready it flips
to named archives (chi-camera-ready). Write both versions on the same day so the
promises match.
Verification before the claim
# The availability statement is a claim; test it like one.
ls protocol/ instruments/ codebook/ data/ analysis/ # inventory vs statement
grep -rEin 'available (upon|on) request' paper/ && echo "WEAK: replace or justify"
python3 -m venv /tmp/repro && /tmp/repro/bin/pip install -r analysis/requirements.txt \
&& /tmp/repro/bin/python analysis/reproduce_tables.py # cold-start the pipeline
grep -rEil 'participant|P[0-9]+_(name|email)' data/ | head # de-identification sweep
"Available upon request" earns no credit at CHI — studies of such promises across fields show most requests go unanswered, and reviewers know it. Either deposit the artifact or explain the genuine constraint.
Output format
[Protocol layer] complete / gaps: <missing instruments>
[Analysis layer] pipeline runs cold: yes/no · codebook/audit trail: yes/no
[Data rung] 1-4 on the ladder + one-line justification
[Anonymized-review versions] links safe for PCS: yes/no
[ADR-Method exposure] low/med/high — <the opaquest spot in the methods>
[One-day fixes] <cheapest transparency wins available now>
Version History
- 9f86f09 Current 2026-07-19 14:39
