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cs-venue-strategy

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Original

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Translation

Chinese

CS Venue Strategy

CS学术发表平台策略

Computer science venues reward different contribution shapes. Use this skill to choose a realistic target and adapt the paper's evidence, tone, and framing.
不同的计算机科学学术发表平台对研究成果的类型偏好不同。运用此技能选择合适的目标平台，并调整论文的论据、语气和框架。

Read First

先读文档

  • references/venue-strategy-policy.md
  • references/research-positioning-policy.md
  • references/venue-strategy-policy.md
  • references/research-positioning-policy.md

Workflow

工作流程

  1. Identify field and subfield: ML, NLP, HCI, systems, SE, security, databases, IR, theory, PL, robotics, graphics, education, or interdisciplinary CS.
  2. List plausible venues and tracks with deadlines, format, review model, and artifact expectations.
  3. Compare audience fit, novelty bar, methodological norms, page limits, and risk.
  4. Map contribution type to venue expectations.
  5. Record the decision in 
    docs/venue/venue-strategy.md
    .
  6. Update paper/research design if venue fit requires stronger baselines, clearer theory, user study, artifact, or threat analysis.
  1. 确定研究领域及子领域：ML、NLP、HCI、系统、SE、安全、数据库、IR、理论、PL、机器人学、图形学、教育或跨学科计算机科学。
  2. 列出可行的发表平台及专题，包括截止日期、格式、评审模式和成果物要求。
  3. 比较受众适配性、创新性要求、方法论规范、页数限制和投稿风险。
  4. 将研究成果类型与平台期望相匹配。
  5. 将决策记录在
    docs/venue/venue-strategy.md
    中。
  6. 如果平台适配性需要更强的基准模型、更清晰的理论、用户研究、成果物或威胁分析，则更新论文/研究设计。

Venue Fit Checks

平台适配性检查

  • Does the venue value this contribution type?
  • Are the baselines and datasets credible for this venue?
  • Does the paper need artifact evaluation or reproducibility badges?
  • Is the novelty claim too incremental for the main track?
  • Would a workshop, demo, findings track, or journal be more honest?
  • 该平台是否重视此类研究成果？
  • 基准模型和数据集是否符合该平台的可信度要求？
  • 论文是否需要成果物评估或可复现性认证？
  • 创新性声明对于主专题来说是否过于增量式？
  • 选择研讨会、演示专题、研究成果专题或期刊是否更贴合实际？

Do Not

注意事项

  • Choose a venue only by prestige.
  • Ignore track-specific review criteria.
  • Reframe after experiments without checking whether evidence still supports the claim.
  • 不要仅根据声誉选择发表平台。
  • 不要忽视专题特定的评审标准。
  • 不要在实验完成后调整论文框架，却不验证论据是否仍能支撑论点。