franklee16/academic-research-skills/acm-ieee-joint-conference-on-digital-libraries

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Use when targeting ACM/IEEE Joint Conference on Digital Libraries (JCDL) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for digital libraries.

Install

npx vskill@latest install franklee16/academic-research-skills/acm-ieee-joint-conference-on-digital-libraries

SKILL.md

ACM/IEEE Joint Conference on Digital Libraries (JCDL)

Conference positioning

ACM/IEEE Joint Conference on Digital Libraries (JCDL) is a top computer-science conference venue for digital libraries, archives, scholarly communication, metadata, preservation, and information access. It rewards a digital-library paper with collection, metadata, access, or preservation contribution. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.

Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.

When to trigger

  • The author names JCDL / ACM/IEEE Joint Conference on Digital Libraries as the target venue.
  • A manuscript in digital libraries needs a conference-fit read before being formatted or submitted.
  • The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
  • The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.

Scope & topic fit

  • Core fit: digital libraries, archives, scholarly communication, metadata, preservation, and information access.
  • Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
  • The paper should explain why the result matters to JCDL's reviewers, not just why it is interesting to the authors' lab or product context.
  • Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
  • If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.

Venue-specific calibration

  • Reviewer lens: Treat JCDL as a digital libraries venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
  • Contribution hook to foreground: the venue-specific contribution bar.
  • Scope vocabulary to use naturally in the abstract and introduction: digital libraries, archives, scholarly communication, metadata, preservation, and information access.
  • Distinctive fingerprint for reviewer calibration: digital, libraries, archives, scholarly, communication, metadata, preservation, information, access, venue-specific, contribution, jcdl.
  • Official anchor domain: www.jcdl.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

Method & evidence bar

  • Use task-appropriate baselines, multiple datasets or languages when the claim is broad, and error analysis that explains model behavior.
  • For LLM work, control for data leakage, prompt sensitivity, evaluation contamination, and human-evaluation reliability.
  • For resources, document annotation, licensing, demographics, quality control, and intended use.
  • For JCDL, the evidence must support the venue-specific signature: a digital-library paper with collection, metadata, access, or preservation contribution.
  • Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

Structure & house style

  • State the language phenomenon, task, or system behavior before the model name.
  • Connect examples to measured errors; reviewers dislike anecdotal examples presented as evidence.
  • Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
  • The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
  • Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.

Official-cycle checklist

  • Open the live official venue page: https://www.jcdl.org/
  • Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
  • Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
  • Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
  • If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

  • [ ] One sentence states why this manuscript belongs at JCDL, using the venue's scope rather than generic "top conference" language.
  • [ ] The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
  • [ ] Related work includes the nearest current-cycle digital libraries papers and explains the technical delta.
  • [ ] The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
  • [ ] The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.

Common desk-reject triggers

  • Evaluation that is only a prompt table or cherry-picked generation examples.
  • Missing dataset documentation, licensing, or annotation reliability.
  • Claims of general language understanding from narrow English-only benchmarks.
  • Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
  • A contribution framed for a neighboring field while giving JCDL reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses JCDL's bar, compare against annual-meeting-of-the-association-for-computational-linguistics / conference-on-empirical-methods-in-natural-language-processing / north-american-chapter-of-the-association-for-computational-linguistics / european-chapter-of-the-association-for-computational-linguistics. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.

Output format

text
[Fit] High / Medium / Low (one-line reason)
[Target] ACM/IEEE Joint Conference on Digital Libraries (JCDL)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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