Liz Earle
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no black box
We don't rate products ourselves. We collect what each public rater already says, put every scale on common ground, and keep the disagreements visible instead of averaging them away.
Collect each rater's own score
Every source keeps its native verdict for this product. We never invent a score for an axis a source didn't measure.
Normalize to 0–100, higher = better
Raters can use different scales and directions: some where a low number means safe, some where a high one does. We map them all onto one 0–100 axis and flip the ones that run backwards so the numbers are finally comparable.
Average per axis, but keep the spread
Scores on the same axis become one representative number (their mean). When raters split by 20+ points we flag it and keep every rater's number. A disagreement is never blended into silence.
Your weights make the overall
The ring is the weighted average of the four pillars using your weights. It's computed fresh every time and never stored, so there's no hidden “objective” default.
Tune your weights
composite recomputes live
+No rater covers this axis for this product, so its weight can't move the score.
No rater covers this axis for this product, so its weight can't move the score.
No rater covers this axis for this product, so its weight can't move the score.
No rater covers this axis for this product, so its weight can't move the score.
No axis has both data and weight. Bring one slider above zero to see a composite.