How blind ranking works
Blind ranking is the most statistically accurate way to measure if Remote Viewing results are better than chance.
The judging pipeline
From the moment you submit a session to the rank you see on your session page, in five steps.
Your target is drawn from the pool
Every session starts the same way: a target is picked for you from the target pool, and you never see it. You record your impressions blind, then upload your work. Your drawings, notes, and PDF pages go to the judge exactly as you submitted them.
Nine decoys join from the same pool
Nine decoy targets are drawn at random from the same pool your target came from. Together with the real target they form a lineup of 10 candidates, and any one of them could plausibly have been the target you were viewing.
The lineup is shuffled
Before the AI sees anything, the 10 candidates are shuffled into a random order. The judge is never told which one is real. From its point of view, every candidate is equally likely.
The AI ranks all 10
A multimodal AI studies your session next to every candidate and orders the full lineup from best match to worst. A separate verification pass then checks the reasoning against each ranked target, and the ranking is redone if it fails.
Your rank is the result
Your score is simply where the real target landed in that blind ranking. #1 means your session pointed at the real target more strongly than at any decoy. Pure chance averages around #5 to #6, so ranks 1 to 3 suggest a strong hit.
Why the judge stays blind
An AI that knows the answer can talk itself into finding it. If we showed the judge your session and the real target side by side and asked "do these match?", it would have every incentive to say yes, and no way to calibrate what a coincidental match looks like.
Blind ranking removes that failure mode. The judge receives 10 candidates in a shuffled order with no labels, so from its perspective any of them could be the real target. The only way the real target ends up at #1 is if your session genuinely describes it better than 9 plausible alternatives.
This mirrors how the original research at Stanford Research Institute scored sessions: an independent judge, blind to the answer, matched transcripts against a set of candidate targets. We run the same protocol with a multimodal AI, which makes the scoring instant, consistent, and free of human bias.
Reading your rank
Your result is the real target's position in the ranking, so lower is better. With 10 candidates and no psi effect at all, the real target would land at each position equally often and average around #5 to #6.
Perfect pick
Your session matched the real target more strongly than every decoy.
Strong hit
The real target beat almost the whole lineup. Well above chance.
Above chance
Better than the random average, but not a decisive match.
Around chance
The session did not distinguish the real target from the decoys.
A single session tells you little either way. Chance produces the occasional #1 and skilled viewers still land mid-pack sometimes. The signal is in the distribution: a viewer who keeps landing in the top 3 across many sessions is doing something chance cannot explain.
Details that keep it honest
Raw session files
The judge works from your uploads exactly as you submitted them: drawings, handwritten notes, and PDF pages. Nothing is summarized or cleaned up first.
Fresh random decoys
The 9 decoys are drawn at random from the target pool for every session, so there is no fixed lineup to learn or game.
Verified reasoning
After ranking, a separate verification pass checks that the judge's reasoning for each of the 10 targets actually matches that target. This ensures the judge doesn't hallucinate its reasoning. If any of the 10 don't match, the whole judging is re-run.
Reasoning you can read
Once the target is revealed, you can open the judge notes on your session page and read why the judge ranked the real target where it did.
One limitation worth knowing: targets without an image cannot be scored this way, because a lineup where only the decoys have images would bias the judge. Sessions on those targets skip blind ranking.
Related topics
What is remote viewing?
The practice this judge scores
Displacement
When viewers describe a decoy instead
Target coordinates
How blind targets are assigned
Psi research
The science behind blind judging protocols
Associative remote viewing
Judging applied to predictions
Platform statistics
Rank distributions across all sessions
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