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Argument from Verbal Classification

Also Known As: definitional argument argument from categorization labeling argument
Argumentation Scheme ID: argument_from_verbal_classification

Definition

The argument from verbal classification assigns an entity to a category based on a definition or label, and then attributes properties of that category to the entity. Once something is classified under a particular term, all the connotations and standard properties of that term are transferred to the specific case. This scheme is powerful because naming things shapes how we think about them, but it can be misused by applying labels that are technically applicable but misleading or by exploiting ambiguity in classification criteria.

Examples

This new regulation requires businesses to report carbon emissions. Requiring reporting is a form of regulation. Regulation is government overreach. Therefore, this carbon reporting requirement is government overreach.

A politician opposing a public library's new digital lending program argues: 'Lending e-books is a form of distribution. Distribution of content is publishing. Publishing is a commercial activity. Therefore, the library is engaging in commercial activity and should be subject to the same copyright licensing fees as a retail business.'

A talk radio host argues against a proposed sugar tax: 'Taxing a specific food product is a form of social engineering. Social engineering is the government controlling your behavior. Controlling your behavior is an attack on personal freedom. Therefore, this sugar tax is an attack on your freedom and must be rejected.'

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Is the subject being placed into a specific category or labeled with a term?

    Type: binary
  2. 2

    Does the classification accurately capture the relevant features of the subject?

    Type: binary
  3. 3

    Are properties of the category being automatically transferred to the subject?

    Type: binary
  4. 4

    Could an alternative classification lead to different conclusions?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.