Secondary patterns

📘 ALSO RUNS (EDGE / SECONDARY PATTERNS)

Pattern	Core Function	Example
Comparison	Evaluate similarity or difference between entities along a shared dimension	This model performs better than the previous version under noisy conditions.
Causal Explanation	State or explain a direct causal relationship between events or states	The system failed because the input data was corrupted.
Causal Inference	Infer likely causal relationships from evidence with uncertainty	The results suggest that increased exposure may lead to improved retention.
Abstraction Shift	Move from a concrete instance to a higher-level conceptual generalization	Remote work reduced commuting time for many employees. More broadly, it reflects a shift toward workplace autonomy.
Tentative Conclusion Framing	Present conclusions with reduced certainty or hedged epistemic commitment	Taken together, the evidence suggests that stricter regulation is likely beneficial, although uncertainties remain.

11. Comparison

Pattern
Comparison

Core Function
Evaluate similarity or difference between two or more entities along a shared dimension.

Canonical Examples

This model performs better than the previous version under noisy conditions.

Compared to traditional methods the new approach is more stable.

Ask Yourself

• What dimension am I comparing on

• Am I measuring similarity or difference

• Is the comparison explicit or implied

Resources

• better than, worse than

• compared to, relative to

• as X as, not as X as

• similarly, likewise

Danger Zone

• Unclear comparison baseline

• Mixing comparison with evaluation without signaling

• False equivalence across unrelated dimensions

What this pattern is NOT
Not evaluation itself, not generalization, not distinction (though related).

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12. Cause–Effect Explanation

Pattern
Causal Explanation (Explicit Causation)

Core Function
State or explain a direct causal relationship between events or states.

Canonical Examples

The system failed because the input data was corrupted.

Increased load led to slower response times.

Ask Yourself

• Am I explaining why something happened

• Is the causal link explicit or inferred

• Is this explanation or just correlation

Resources

• because, since, as

• lead to, result in, cause

• due to, owing to

• therefore (when causal not inferential)

Danger Zone

• Confusing correlation with causation

• Oversimplified causal chains

• Hidden multi-factor causation

What this pattern is NOT
Not inference (though closely related), not generalization.

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13. Causal Inference (Blended Form)

Pattern
Causal Inference (Probabilistic / Reasoned Causality)

Core Function
Infer likely causal relationships from evidence rather than stating direct causation.

Canonical Examples

The pattern suggests that increased exposure may lead to improved retention.

The results indicate a possible causal link between training and performance.

Ask Yourself

• Am I stating causation or inferring it

• Is the evidence sufficient for causal interpretation

• Have I signaled uncertainty appropriately

Resources

• suggests that, indicates that

• may lead to, could result in

• is associated with

• appears to cause

Danger Zone

• Overstating causal certainty

• Treating association as causation

• Missing epistemic hedging

What this pattern is NOT
Not direct causation, not pure inference, not evaluation.

14. Abstraction Shift (Concrete → Abstract Mapping)

Function
Moves from a specific instance or example to a higher-level conceptual generalization.

Core markers / structures

• “more broadly…”

• “this reflects…”

• “at a general level…”

• example → principle transitions

• nominalization lift (X happened → X represents Y)

Canonical examples

Remote work reduced commuting time for many employees. More broadly, it reflects a shift toward workplace autonomy.

This specific failure points to a deeper structural issue in system design.

Enables
Generalization, Reframing, Analytical Decomposition

Nature
Cross-level discourse operation linking concrete instances to abstract conceptual structures.

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15. Tentative Conclusion Framing

Function
Presents conclusions with reduced epistemic force, signaling uncertainty or limited commitment.

Core markers / structures

• “suggests that…”

• “likely…”

• “taken together, the evidence suggests…”

• “although uncertainties remain…”

• hedged conclusion packaging

Canonical examples

Taken together, the evidence suggests that stricter regulation is likely beneficial, although important uncertainties remain.

The results indicate a possible improvement in performance under constrained conditions.

Enables
Qualification, Inference, Evaluation

Nature
Epistemic stance packaging of conclusions across multiple linguistic systems (modality + evidentiality + discourse framing).

Note

ALSO-RUNS vs TIER 1 — DECOMPOSITION MAPPING

Also-run Pattern	Why it is not Tier 1	Decomposition into Tier 1 patterns
Comparison	Requires a shared dimension + evaluative alignment, not a primitive operation	Evaluation + Distinction
Causal Explanation	Depends on establishing a causal link plus structured inference	Inference + Analytical Decomposition
Causal Inference	Probabilistic causal interpretation from evidence, not atomic	Inference + Qualification + Evidence Attribution
Abstraction Shift	Moves between levels of generality, built on existing generalization/reframing mechanisms	Generalization + Reframing
Tentative Conclusion Framing	Global epistemic packaging of conclusions, not a single reasoning move	Qualification + Inference + (optional Evaluation)