Reproducibility at CSCW cannot mean "rerun my script, get my table" — most of the venue's evidence is people, and much of it must never leave the research team. The venue's real standard is auditability: a skeptical reader should be able to see how you got from data to claims, and to build on the work, even where they cannot re-execute it. Different strands of a paper owe different transparency debts.
| Evidence strand | Shareable | Auditable instead of shareable |
|---|---|---|
| Interviews / fieldwork | Interview guide, recruitment text, codebook with definitions and example (paraphrased) excerpts | The analysis trail: coding approach, memo practice, how disagreements were resolved, how themes stabilized |
| Trace / log analysis | Pipeline code, query definitions, aggregated datasets, synthetic samples | Exact API/version/date of collection; filtering decisions with counts at each step; bot/deletion handling |
| Surveys | Full instrument, scale provenance, analysis scripts | Sampling frame, response/nonresponse accounting |
| Deployments | System code or architecture description, condition assignment logic | Site-selection reasoning; what the deployment context makes non-portable |
| Statistics anywhere | Analysis scripts keyed to each table/figure | Pre-specification vs. exploration, stated honestly |
You cannot share transcripts; you can share how you thought. The auditable minimum for interpretive work:
Platform data rots. Reviewers and future researchers need the ledger even when the data cannot travel:
[Source] platform, endpoint/API version, collection dates
[Scope] query terms / community list / time window, with the WHY
[Attrition] rows at each filter step: raw → deduplicated → bot-filtered →
analysis set (counts, not adjectives)
[Constructs] each analysis variable → the raw field(s) it derives from →
the practice it is claimed to measure
[Fragility] what breaks if the platform changes (API terms, deletion policy)
[Release] what is shared: code / aggregates / synthetic sample / nothing + reason
Write the data statement as a truth-telling exercise, not boilerplate. Three honest shapes:
What never survives review twice (remember the same reviewers return at R&R): "data available upon reasonable request" with no request path, and claims of sharing that the supplement does not actually contain.
For confirmatory quantitative strands, preregistration strengthens the paper — link it anonymized (registries support anonymous view links). Do not force exploratory or interpretive work into a preregistration costume; labeling exploration honestly is the venue's norm.
[Per strand] shareable artifacts listed and actually present? y/n
[Qualitative] codebook + decision log exist? tradition named? y/n
[Trace] ledger complete incl. attrition counts? y/n
[Statement] availability text matches reality exactly? y/n
[Ethics gate] every shared artifact re-checked against consent scope? y/n
Run the gate last and strictly: a transparency package that violates a consent
agreement is not a reproducibility win, it is a research-ethics failure that
cscw-artifact-evaluation exists to prevent.