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Research Specification Integrity Checker
An automated tool that analyzes submitted research papers (or their code/data) to detect specification-searching patterns — where authors tried many regression combinations and selectively reported favorable results. It flags papers where reported results are fragile across reasonable specification variations, helping journals and institutions maintain research integrity in the age of AI-assisted p-hacking.
Why this matters
You are a journal editor in economics drowning in submissions, many of which now use AI to rapidly iterate through regression specifications until finding one that produces a publishable result. You suspect p-hacking is rampant but have no tool to systematically check whether a paper's results survive reasonable specification variations. Peer reviewers manually check robustness, but they cannot run hundreds of alternative specifications. What you need is an automated system that takes a paper's data and code, re-runs the analysis across plausible alternative specifications, and gives you a fragility score — showing whether the headline result is robust or cherry-picked from a garden of forking paths.
- · Built for Academic journal editors, research integrity officers at universities, reproducibility-focused research organizations, and meta-science researchers.
- · Most likely monetization: SaaS subscription for journals and institutions, with a free tier for individual researchers to self-check.
The Pain · Narrative
You are a journal editor in economics drowning in submissions, many of which now use AI to rapidly iterate through regression specifications until finding one that produces a publishable result. You suspect p-hacking is rampant but have no tool to systematically check whether a paper's results survive reasonable specification variations. Peer reviewers manually check robustness, but they cannot run hundreds of alternative specifications. What you need is an automated system that takes a paper's data and code, re-runs the analysis across plausible alternative specifications, and gives you a fragility score — showing whether the headline result is robust or cherry-picked from a garden of forking paths.
Score Breakdown
Market Signal
Go-to-Market
Managing editors of mid-tier economics and political science journals who are concerned about declining submission quality due to AI-assisted research
~500-1000 journals globally in quantitative social sciences concerned with research integrity, plus ~5-10K researchers who would self-check before submission
Direct outreach to journal editors and research integrity officers at top-50 universities
$199/month for journal-tier subscriptions, $15/month for individual researcher self-check tier
3 journal editorial teams agree to pilot the tool on incoming submissions within 60 days
MVP Scope · 1–2 weeks
- Build core analysis engine that ingests R/Stata/Python replication code and re-runs regressions across generated specification variations
- Implement specification variation generator: alternate control variables, functional forms, subsample definitions, and clustering choices
- Create fragility scoring algorithm that summarizes how robust the headline result is across specifications
- Build simple CLI tool that researchers can run on their own code before submission
- Test on 5 published economics papers with publicly available replication data to calibrate fragility scores
- Build web interface for paper upload with code/data package and automatic analysis execution
- Generate human-readable integrity report with fragility heatmap and specification coefficient plot
- Add support for the most common econometric frameworks (fixed effects, IV, diff-in-diff, RDD)
- Create self-check tier where researchers run the tool pre-submission and get a report to share with editors
- Reach out to 20 journal editors and 10 research integrity officers for pilot feedback
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Most papers do not include replication code and data, making automated specification checking impossible without the raw materials — the open science movement has not progressed far enough in economics.
- 2Journal editors may resist tooling that flags accepted papers as fragile, creating institutional friction and reputational risk for the tool's adopters.
- 3Determining 'plausible' specification variations is inherently subjective — the tool could be gamed by defining the specification space narrowly, or could produce false positives that anger honest researchers whose legitimate specifications are flagged.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
One commenter directly described how AI tools now make it trivially easy to try every possible regression combination until a desired result appears, identifying this as a growing problem in economics research. The commenter framed this as a negative consequence of AI adoption in academia. While only one commenter raised this specific concern, it aligns with a well-known broader crisis of research reproducibility in quantitative social sciences, and the commenter's framing suggests this problem is worsening with AI tooling.
Action Plan
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Landing Page Copy Kit
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Headline
Research Specification Integrity Checker
Sub-headline
An automated tool that analyzes submitted research papers (or their code/data) to detect specification-searching patterns — where authors tried many regression combinations and selectively reported favorable results. It flags papers where reported results are fragile across reasonable specification variations, helping journals and institutions maintain research integrity in the age of AI-assisted p-hacking.
Who It's For
For Academic journal editors, research integrity officers at universities, reproducibility-focused research organizations, and meta-science researchers
Feature List
✓ Upload paper + supplementary code/data for automated specification robustness analysis ✓ Re-run regressions across plausible specification variations and report result fragility ✓ Detect patterns consistent with specification searching (e.g., selective covariate inclusion) ✓ Generate integrity report for editors/reviewers with fragility heatmap ✓ Integration with Stata/R/Python analysis scripts
Where to Validate
Share your landing page in r/HN · front_page — that's exactly where these pain points were discovered.
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