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conference-submission

Category: review
Field: economics
License: private (curator-owned)
Updated: 2026-05-20
Stages: referee-simulation

Curator-private skill — copy text from 100xOS/shared/skills/review/conference-submission.md.

Conference Submission Checklist

HumanxAI Finance Conference Requirements

Submission Materials

  1. Extended abstract or full paper (PDF format)
  2. AI workflow description: Document how AI tools were used in the research process
  3. LaTeX source files (if full paper)

Extended Abstract Requirements (if submitting abstract)

  • 2-4 pages, single-spaced
  • Must include: research question, contribution, method, preliminary results
  • References do not count toward page limit
  • Use standard academic formatting (12pt, reasonable margins)

Full Paper Requirements (if submitting paper)

  • Standard academic paper format
  • No strict page limit, but conciseness is valued
  • Must include all standard sections: introduction, literature, data, method, results, conclusion
  • Tables and figures should be publication-quality

AI Workflow Documentation

The conference specifically values transparency about AI use. Document: - Which AI tools were used (Claude Code, etc.) - What tasks were delegated to AI (literature search, code generation, writing assistance, etc.) - How human judgment directed and validated AI outputs - Quality control measures (human review cycles, verification steps) - What the human researcher contributed vs. what AI contributed

This is a feature, not a bug — the conference celebrates human-AI collaboration. Be specific and honest.

Pre-Submission Checklist

Content Quality

  • Research question is clearly stated in the first paragraph
  • Contribution is articulated in 1-2 sentences
  • Identification strategy is explicit and defended
  • Results are presented with appropriate uncertainty (confidence intervals, standard errors)
  • Economic magnitude is discussed, not just statistical significance
  • Limitations are acknowledged honestly
  • Literature positioning covers the 3-5 closest prior papers

Data & Methods

  • Data sources are documented (reproducibility)
  • Sample construction is transparent (inclusions, exclusions, time period)
  • Summary statistics table is complete (N, mean, SD, min, max)
  • Estimation method matches the research question
  • Standard errors are clustered/robust as appropriate
  • At least one robustness check per major identification threat

Writing Quality

  • Introduction accomplishes all 5 tasks (question, gap, method, results, roadmap)
  • No filler phrases or hedging ("it is important to note that...")
  • Active voice throughout
  • Every table and figure is discussed in the text
  • Consistent notation and terminology
  • Abstract is self-contained and informative (question, method, finding)

Formatting

  • PDF compiles without errors
  • All figures are vector graphics or high-resolution (300+ DPI)
  • Tables are properly formatted (no vertical lines, minimal horizontal rules)
  • References are complete (no "et al." in reference list, only in citations)
  • Page numbers are present
  • Author information matches submission system

AI Workflow Document

  • Lists all AI tools used with version/model information
  • Describes the pipeline: idea → design → literature → data → estimation → writing
  • Explains human checkpoints and quality gates
  • Honest about AI limitations encountered
  • Describes the iterative refinement process

Common Rejection Reasons at Finance Conferences

  1. Unclear contribution: Referee cannot state in one sentence what the paper adds
  2. Weak identification: Causal claims without credible identification strategy
  3. Overfitting to crypto: Results are about crypto-specific phenomena with no broader economic insight
  4. Missing benchmarks: No comparison to existing methods or baselines
  5. Scope too broad: Paper tries to answer too many questions
  6. Poor writing: Verbose, poorly organized, unclear exposition
  7. Insufficient robustness: Only one specification, no sensitivity analysis
  8. Stale data: Results based on data that is several years old without justification