G: TL;DR
Success is goal-relative and internal to a frame. Victory is comparative and external to a contest.
A trait can cleanly divide a subset because the context is constrained, but become meaningless—or flip sign—when applied to the entire set because the reference frame changes.

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Success vs. Victory

Success

• Defined against your own objective.
• Can occur without an opponent.
• Multiple actors can be successful at the same time.
• Stable across time if the goal doesn’t move.

Victory

• Defined by outperforming others.
• Requires a contest or comparison.
• Usually zero-sum (or at least rank-ordered).
• Can exist without meaningful success (you win, but nothing worthwhile was achieved).

You can succeed without winning, and you can win without succeeding.

日本語（にほんご）
成功（せいこう） は 自分（じぶん）の 目標（もくひょう）に 対（たい）して 決（き）まる。— Success is defined relative to one’s own goal.

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The Subset Attribute Problem (the word you’re circling is context collapse or scale dependence)

Clean separation inside a subset

Subset: Graduate students in a physics program
Attribute: “Has published a first-author paper”

Within this subset:

• ✔ Accurately divides students into research-ready vs. still training.
• The attribute tracks something real and relevant.
• The local context is narrow and aligned with the goal of the group.

This is a valid discriminator because the subset shares assumptions, constraints, and incentives.

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Breakdown at the level of the whole set

Whole set: All adults

Apply the same attribute:

• “Has published a first-author physics paper”

Now:

• ❌ The division is arbitrary.
• ❌ It excludes vast numbers of highly capable people.
• ❌ It may even invert meaning (artists, engineers, tradespeople, caregivers are mislabeled as “less capable”).

The attribute no longer measures competence, readiness, or value.
It measures exposure to a niche pipeline.

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Why this happens

1. Context collapse
   The attribute was meaningful only because the subset shared goals and norms.

2. Scale dependence
   Properties that discriminate well at small scales often fail at larger ones.

3. Reference-frame inversion
   What signals excellence locally can signal irrelevance globally.

A classic compact phrasing:

A good classifier inside a niche becomes noise—or propaganda—when exported.

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One more sharp example (even clearer)

Subset: Professional chess players
Attribute: “Plays aggressively”

• Inside the subset: cleanly separates tactical players from positional ones.
• In the whole population: meaningless or misleading, since most people don’t play chess at all.

The attribute doesn’t generalize; it overfits its original domain.

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Bottom line

• Success answers: Did I accomplish what I set out to do?
• Victory answers: Did I outperform others under agreed rules?
• Attributes that divide subsets well often do not scale, and when forced to scale, they turn arbitrary, exclusionary, or inverted.

This is how reasonable metrics become bad ideology when they escape their native context.