# Generality | Envisioning Vocab

Source: https://www.envisioning.com/vocab/generality

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# Generality

A lightweight score for how broad (general) vs. narrow (specific) a vocab term is.

## What it means

**Generality** is our attempt to answer: “Is this term a foundational idea that many other concepts depend on, or a more specific technique / variant / implementation detail?”

Higher generality corresponds to broader, more foundational concepts (e.g. “Machine Learning”, “Neural Networks”). Lower generality corresponds to narrower or more specialized concepts (e.g. fine‑tuning methods, particular architecture variants).

## How we score it

Each term is scored from its **title** and **summary**. We ask a model for **7 independent scores** on a 0–1 scale (with some randomness enabled), then:

-   Clamp each score to **\[0, 1\]**
-   Convert each score to an integer **0–1000**
-   Average the 7 clamped scores and convert that average to **0–1000**

The final stored value is that **0–1000 average**. (We keep the 7 raw draws separately so we can see variance.)

## How to use it

Generality is best used for **ranking and sorting** (“show me the most foundational concepts first”), not as a precise scientific measurement. Two close scores can be effectively tied.

## Caveats

-   **Model judgment**: the score reflects a model’s conceptual judgment from a short summary, not a full literature review.
-   **Some variance is intentional**: taking 7 draws and averaging makes the score more stable than a single draw.
-   **Scope matters**: “general” here means general within AI/ML concepts, not across all of science.

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