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Get Started Free →Use when deciding whether a project belongs at CSCW — is the group, community, or organization the real unit of analysis? — and running the routing decision against CHI, ICWSM, DIS, GROUP, TOCHI, and social-science journals before any drafting or platform account setup begins.
.claude/skills/brycewang-stanford-cscw-topic-selection/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 78% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 23% | 0% |
CSCW publishes human-centered research on technology that supports or reshapes social, cooperative, and collaborative practice. The venue test is not "does this involve people and computers" — nearly everything does — but whether the paper's explanatory action happens at the level of a group, community, organization, or network. Run this decision before writing, because the framing it produces changes the introduction, the methods, and even which platform you submit through.
Ask three questions of the project's core claim:
team, a community, a moderation crew, a platform's user population, or an organization, CSCW is plausible. If it is completed by "users" as interchangeable individuals, you are probably describing a different venue's paper.
labor, governance, trust, awareness, and collective sense-making. A faster or prettier interaction with no social consequence is not the venue's question.
about cooperative practice that outlives the specific system studied. If your contribution evaporates when the platform changes its UI, sharpen the social claim first.
| Signal in your project | Better home | Why | | --- | --- | --- | | The novelty is an interaction technique, input method, or interface design | CHI (or UIST for technique depth) | CSCW reviewers will ask "where is the group?" and find no answer | | Large-scale social-media measurement with no design or cooperative-work stake | ICWSM | Measurement-first culture; CSCW wants the collaboration consequence drawn out | | A designed artifact where the argument is the design process itself | DIS | Design-research epistemology has its own reviewing home | | Smaller-scope group-work study wanting a community conversation | GROUP | Same intellectual family, different scale and pacing | | A mature line needing unlimited space and no conference tie | TOCHI or a social-science journal | CSCW already runs journal review, but PACMHCI CSCW papers carry a conference presentation expectation | | Governance/harms analysis where accountability frameworks are the core | FAccT | Policy-normative reviewing culture fits better |
The CHI/CSCW boundary is the one most authors get wrong, in both directions. Both venues accept qualitative and quantitative work about people and technology. The working separation: CHI's center of gravity is the human-computer interaction — capability, interface, individual experience; CSCW's is the human-human arrangement that computing mediates. A study of how a screen-reader user navigates a document is CHI-shaped; a study of how blind and sighted colleagues co-edit that document is CSCW-shaped. When a project genuinely supports both framings, choose by which literature you need reviewers to know deeply — and note that as of 2026 the two venues differ structurally: CHI runs an annual deadline, while CSCW has moved to rolling journal-style submission (see cscw-workflow).
The venue's Lasting Impact lineage (see resources/exemplars/library.md) spans organizational analysis, concept development, theory for design, sensitive-community fieldwork, and large-scale trace analysis. All five registers are first-class. What they share is not a method but a shape: an empirical or analytic account of cooperative practice that other researchers can build with.
Weak-fit signals, regardless of topic:
"group" exists only as an aggregate statistic.
practices, norms, or stakes — extraction without engagement.
Run this before committing to the venue:
text[Claim owner] Complete: "This paper shows that <group/community/org> ___" If no collective completes it honestly → re-route or re-frame. [Mediation] Name the cooperative practice the technology touches (coordination / governance / awareness / trust / labor). [Survival test] State the finding with the platform name deleted. Still interesting? → CSCW-shaped. [Nearest neighbor] Name the ONE venue you'd choose if CSCW rejected the framing, and why its reviewers differ. [Regime check] Confirm the current submission pathway (rolling via Manuscript Central as of 2026-07-08) before planning dates.
text[CSCW fit] Strong / Conditional / Weak — one sentence on the unit of analysis [Cooperative practice at stake] <named practice> [Contribution kind] organizational analysis / concept / theory-for-design / community study / trace analysis / system [Re-route candidate] <venue + the reviewer-culture reason> [Framing repair] <the single change that would most improve CSCW fit>
Facts here reflect cscw.acm.org and the rolling CFP as checked on 2026-07-08; the venue is mid-transition, so re-verify submission mechanics whenever routing advice turns into a plan.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 19,453 | 11,360 | -42% | 1 | 1 | 0% | 1,926 | 2,294 | +19% | 0 | 0 | — |
case-02 | fail→pass | 15,070 | 12,742 | -15% | 1 | 1 | 0% | 1,710 | 2,578 | +51% | 0 | 0 | — |
case-03 | fail→fail | 15,345 | 14,053 | -8% | 1 | 1 | 0% | 1,548 | 2,411 | +56% | 0 | 0 | — |
case-04 | pass→pass | 19,783 | 21,326 | +8% | 1 | 1 | 0% | 2,539 | 3,552 | +40% | 0 | 0 | — |
case-05 | pass→pass | 20,991 | 25,322 | +21% | 1 | 1 | 0% | 3,012 | 4,184 | +39% | 0 | 0 | — |
case-06 | pass→pass | 15,764 | 17,060 | +8% | 1 | 1 | 0% | 1,890 | 3,032 | +60% | 0 | 0 | — |
case-07 | pass→pass | 21,028 | 21,038 | +0% | 1 | 1 | 0% | 2,613 | 3,967 | +52% | 0 | 0 | — |
case-08 | pass→pass | 15,748 | 13,287 | -16% | 1 | 1 | 0% | 1,510 | 2,224 | +47% | 0 | 0 | — |
case-09 | fail→pass | 18,907 | 11,006 | -42% | 1 | 1 | 0% | 2,008 | 2,299 | +14% | 0 | 0 | — |
case-19 | pass→pass | 12,267 | 7,509 | -39% | 1 | 1 | 0% | 1,556 | 2,287 | +47% | 0 | 0 | — |
case-10 | pass→pass | 17,734 | 11,486 | -35% | 1 | 1 | 0% | 1,706 | 2,369 | +39% | 0 | 0 | — |
case-11 | pass→pass | 19,137 | 13,979 | -27% | 1 | 1 | 0% | 1,790 | 2,525 | +41% | 0 | 0 | — |
case-12 | fail→pass | 19,619 | 11,550 | -41% | 1 | 1 | 0% | 1,841 | 2,184 | +19% | 0 | 0 | — |
case-13 | pass→pass | 37,101 | 13,126 | -65% | 1 | 1 | 0% | 3,042 | 2,261 | -26% | 0 | 0 | — |
case-14 | pass→fail | 21,239 | 13,488 | -36% | 1 | 1 | 0% | 2,470 | 2,375 | -4% | 0 | 0 | — |
case-15 | fail→fail | 13,663 | 9,652 | -29% | 1 | 1 | 0% | 1,263 | 2,029 | +61% | 0 | 0 | — |
case-16 | pass→pass | 17,486 | 13,214 | -24% | 1 | 1 | 0% | 1,671 | 2,279 | +36% | 0 | 0 | — |
case-17 | pass→pass | 14,479 | 11,610 | -20% | 1 | 1 | 0% | 1,713 | 2,162 | +26% | 0 | 0 | — |
case-18 | fail→pass | 8,236 | 11,089 | +35% | 1 | 1 | 0% | 1,167 | 2,072 | +78% | 0 | 0 | — |
case-20 | fail→pass | 20,795 | 16,301 | -22% | 1 | 1 | 0% | 2,002 | 2,457 | +23% | 0 | 0 | — |
case-21 | fail→pass | 21,202 | 11,143 | -47% | 1 | 1 | 0% | 2,136 | 2,077 | -3% | 0 | 0 | — |
case-22 | fail→pass | 20,143 | 7,062 | -65% | 1 | 1 | 0% | 1,955 | 2,412 | +23% | 0 | 0 | — |
case-23 | fail→pass | 17,917 | 10,933 | -39% | 1 | 1 | 0% | 1,901 | 2,117 | +11% | 0 | 0 | — |
case-24 | fail→pass | 13,035 | 12,479 | -4% | 1 | 1 | 0% | 1,701 | 2,492 | +47% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +33 percentage points is the difference between those two pass rates over the 24 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.