1. Short Definition

Classification Coarsening Under Scale occurs when a system’s classification capacity fails to keep pace with load, complexity, speed, or compression, causing lower-resolution decisions and higher misclassification risk.

The system sorts reality too roughly for the consequences involved.

2. Canonical Pattern

Load↑ + Γ capacity insufficient ⇒ classification coarsens

Expanded:

case volume / speed / complexity / compression↑
>
classification capacity
⇒ category resolution↓
⇒ misclassification↑
⇒ hidden debt↑

Plain form:

When classification capacity is exceeded, the system makes rougher distinctions.

3. Mechanic Description

SCALE-056 identifies a major scaling hazard in systems that must sort cases, signals, users, risks, symptoms, claims, events, or states.

Classification is necessary.

Systems must decide:

• what kind of case is this?
• what signal class is this?
• what risk level applies?
• what response path fits?
• what boundary condition is active?
• what exception matters?
• what label is appropriate?
• what intervention is warranted?

But as systems scale, classification burden rises.

More cases, more edge conditions, more diversity, more complexity, more speed, and more pressure all demand greater classification capacity.

If Γ capacity does not scale, the system begins using coarser categories.

This produces:

• false positives
• false negatives
• generic responses
• inappropriate routing
• boundary errors
• escalation errors
• under-response
• over-response
• identity-binding errors
• medical or diagnostic mismatch
• legal or policy misclassification
• AI safety misclassification
• security triage failures
• institutional injustice

Classification coarsening may look efficient because it simplifies decision-making.

But when consequences are high, low-resolution classification creates hidden debt.

4. UTS Variable Mapping

5. Diagnostic Questions

1. What is being classified?
2. Is classification capacity sufficient for the case load?
3. Are categories becoming broader or less precise?
4. Are edge cases being forced into generic buckets?
5. Is throughput pressure reducing nuance?
6. Are misclassification appeals increasing?
7. Are high-consequence cases receiving enough resolution?
8. Can classification decisions be audited?
9. Are wrong labels creating hidden debt?
10. Is classification coarsening being mistaken for efficiency?

6. Failure Signatures

1. Resolution Loss

Γ resolution↓ as Load↑

The system uses lower-resolution categories under scale pressure.

2. Misrouting

Γ_mis ⇒ wrong response path

Cases are sent to the wrong intervention, constraint, or repair path.

3. False Urgency / False Safety

classification coarsening ⇒ over-escalation or under-response

The system misreads risk level.

4. Identity-Binding Error

low-resolution label + identity binding ⇒ H↑

A rough classification binds to a node’s identity or rights.

5. Throughput-Nuance Tradeoff

Φ_throughput↑ while Γ_resolution↓

The system processes more cases by understanding them less.

7. Related Failure Modes

• classification coarsening
• misclassification
• false positive
• false negative
• bad routing
• identity-binding error
• policy misfire
• AI safety misclassification
• medical mismatch
• legal injustice
• hidden debt accumulation
• throughput-coherence divergence

8. Related Diagnostics

9. Restoration Implications

If SCALE-056 is active, restoration requires classification capacity expansion or load reduction.

Required actions:

1. Identify where classification is coarsening.
2. Reduce throughput pressure where consequences are high.
3. Increase classification capacity.
4. Add review layers for edge cases.
5. Preserve appeal pathways.
6. Improve category resolution.
7. Separate low-stakes triage from high-stakes classification.
8. Audit misclassification patterns.
9. Repair harms from wrong labels or routing.
10. Validate recurrence reduction after classification redesign.

Core restoration rule:

Do not increase throughput by lowering classification below consequence resolution.

10. Compact Registry Entry

id: SCALE-056
name: "Classification Coarsening Under Scale"
family: "SCALE-J — Attention, Signals, and Classification Mechanics"
type: "classification-resolution-failure-mechanic"
status: "draft-ready"
short_definition: "Classification capacity failing to keep pace with load, complexity, speed, or compression causes lower-resolution decisions and higher misclassification risk."
canonical_pattern: "Load↑ + Γ capacity insufficient ⇒ classification coarsens"
failure_signature: "case volume/speed/complexity/compression↑ > classification capacity ⇒ category resolution↓ + misclassification↑ + hidden debt↑"
primary_variables:
  - O
  - H
  - ε
  - ι
  - Au
  - µᵢ
  - BΣ
  - K
  - R
  - Φ
primary_diagnostics:
  - Γ_capacity
  - Γ_resolution
  - case_load
  - classification_latency
  - misclassification_rate
  - appeal_rate
  - edge_case_density
  - Au_classification
  - Φ_throughput
  - affected_node_cost
related_failure_modes:
  - classification_coarsening
  - misclassification
  - false_positive
  - false_negative
  - bad_routing
  - identity_binding_error
  - policy_misfire
  - ai_safety_misclassification
  - legal_injustice
  - throughput_coherence_divergence
restoration_implication: "Reduce throughput pressure where necessary, increase classification capacity, improve category resolution, preserve appeals, audit misclassification patterns, and repair wrong-label harms."

11. One-Line Canon

A system becomes incoherent when it handles more cases by understanding each case less.