Yellow Drift treats a word as a search direction, the internet as a visual field,
and the average image as the residue left when thousands of results are compressed into one surface.
Before measuring colour, we tested the method on words with a clear visual image.
Most meanings survived compression. One did not.
Before measuring yellow, we asked a simpler question.
Does visual meaning survive compression?
For one image, the local word for heart was used to retrieve thousands of images from the internet
in six languages. Every result was kept: emoji, anatomy, jewellery, cartoons and noise. The field was then averaged into a single surface.
Different languages. Different search fields. The same image returns.
A heart is a designed symbol, built for recognition. It therefore collapses cleanly into one shape.
That is the easy case. The more revealing test is a word nobody designed.
Next came a word nobody designed: tree.
In each language, the local word for tree became the search direction. There was no curated set of ideal trees.
Whatever the word returned stayed in the dataset.
Five of these words pointed almost entirely at one thing: a tree. The sixth was Dutch.
Here are the six averages side by side. Thousands of images per language, compressed into one surface each. The difference is visible before it is explained.
Spanish, Portuguese, Arabic, Chinese and Danish settle into the same vertical structure: trunk, crown, axis, green.
The Dutch average is warmer, looser and broken. The tree is barely holding together. Something orange is bleeding through it.
This is not a flaw in the method.
It is the method finding something.
The Dutch anomaly does not mean that Dutch people picture trees differently. It means that the word
boom enters a global image field, not a Dutch one.
In Dutch, boom means tree. Online, however, it collides with the international English
boom: the comic-book sound of an explosion. One search direction produces two visual populations competing for the same space.
Drag the line. These are the two image populations behind the Dutch average. Switch to Blend and stop in the middle. That mixture is close to what the averaging produces.
The hard cut keeps the two meanings legible. The blend shows what happens inside the dataset:
two visual archetypes try to occupy one frame, and neither wins cleanly.
The result is diffuse.
Run the two halves of boom separately and the collision becomes visible. One average is still recognisably a tree. The other is an explosion. Inside it, the letters of the word that produced it begin to surface.
This single average is a miniature version of the whole project. Words do not exist in isolation. On the internet, meanings compete for visibility. The compression keeps the score.
Every average is a many-to-one operation.
A word does not point to one image. It points to a field of references: angles, seasons, crops, products, errors and cultures.
All of it is compressed into one surface.
When the field is averaged, detail disappears. If the visual idea behind the word is stable enough, a structure remains.
The degree to which a concept survives this compression is its archetypal density.
Heart has high density and survives intact. Tree has high density in five languages. Dutch boom has low density, because two archetypes share the word and neither dominates. The average becomes a measure of how stable a meaning remains once it passes through the internet.
Yellow Drift applies the same method to colour.
The question is not whether yellow is represented correctly.
It is how stable the visual field of yellow remains when thousands of references
are compressed into one measurable surface, and how far that surface drifts from a fixed point.
That fixed point is #FFFF00. It is not the true yellow and not a correction.
It is simply a stable place to measure from.