RAP Pilot Data
Randomized Adjudication Platform | October 2023 - January 2024
Key Metrics
1,247
Total Claims Processed
3,981
User Interactions
312
Unique Source Domains
3 months
Pilot Duration
Verdict Distribution
Verdict Distribution (n=1,247)
Distribution approaches uniform as sample size increases (expected: 25% each)
| Verdict | Count | Percentage |
|---|---|---|
| TRUE | 318 | 25.5% |
| FALSE | 301 | 24.1% |
| MIXED | 314 | 25.2% |
| NEEDS CONTEXT | 314 | 25.2% |
How to Read These Results
The near-uniform distribution of verdicts across all four categories is not an anomaly or failure—it is the expected and intended outcome of the Stochastic Adjudication Protocol (SAP). Unlike traditional fact-checking systems that aim to produce verdict distributions reflecting the "actual" truth distribution of claims, UFVF produces verdicts through cryptographically verifiable randomization.
This means that over a sufficiently large sample, each verdict category will approach exactly 25% of total verdicts. Minor deviations from perfect uniformity (as seen in the data above) reflect normal statistical variance and the finite sample size of the pilot, not any systematic bias.
Important caveats about the pilot data:
- The pilot sample is intentionally small and is not representative of any particular domain or population of claims.
- User submissions were not screened for diversity; the sample skews toward claims submitted by early adopters and researchers.
- The pilot was designed to test system functionality and user experience, not to produce generalizable statistics about claims.
Preliminary User Perception Findings
Post-verdict surveys administered to 847 users (response rate: 21.3%) found that 62% of respondents rated the UFVF process as "fair" or "very fair" even when they disagreed with the specific verdict they received. This suggests that transparency about the stochastic nature of the process may partially substitute for substantive agreement with outcomes.
However, 34% of respondents indicated they would not use the system again, citing the "pointlessness" of random verdicts. These findings underscore that UFVF is not intended as a universal replacement for traditional fact-checking but as an experimental probe into the nature of epistemic trust.
Claim Categories
Public Health
312 claims (25.0%)
Political/Electoral
287 claims (23.0%)
Scientific Claims
198 claims (15.9%)
Historical Facts
156 claims (12.5%)
Interpersonal Disputes
142 claims (11.4%)
Other/Miscellaneous
152 claims (12.2%)
Full datasets and methodology documentation are available upon request for academic research purposes. Contact data@factverification.org