lyndonkl/negative-contrastive-framing

Negative Contrastive Framing

Workflow

Copy this checklist and track your progress:

Negative Contrastive Framing Progress:
- [ ] Step 1: Define positive concept
- [ ] Step 2: Identify negative examples
- [ ] Step 3: Analyze contrasts
- [ ] Step 4: Validate quality
- [ ] Step 5: Deliver framework

Step 1: Define positive concept

Start with initial positive definition, identify why it's ambiguous or fuzzy (multiple interpretations, edge cases unclear), and clarify purpose (teaching, decision-making, quality control). See Common Patterns for typical applications.

Step 2: Identify negative examples

For simple cases with clear anti-patterns → Use resources/template.md to structure anti-goals, near-misses, and failure patterns. For complex cases with subtle boundaries → Study resources/methodology.md for techniques like contrast matrices and boundary mapping.

Step 3: Analyze contrasts

Create negative-contrastive-framing.md with: positive definition, 3-5 anti-goals, 5-10 near-miss examples with explanations, common failure patterns, clear decision criteria ("passes if..." / "fails if..."), and boundary cases. Ensure contrasts reveal the why behind criteria.

Step 4: Validate quality

Self-assess using resources/evaluators/rubric_negative_contrastive_framing.json. Check: negative examples span the boundary space, near-misses are genuinely close calls, contrasts clarify criteria better than positive definition alone, failure patterns are actionable guards. Minimum standard: Average score ≥ 3.5.

Step 5: Deliver framework

Present completed framework with positive definition sharpened by negatives, most instructive near-misses highlighted, decision criteria operationalized as checklist, common mistakes identified for prevention.

Common Patterns

By Domain

Engineering (Code Quality):

• Positive: "Maintainable code"
• Negative: God objects, tight coupling, unclear names, magic numbers, exception swallowing
• Near-miss: Well-commented spaghetti code (documentation without structure)

Design (UX):

• Positive: "Intuitive interface"
• Negative: Hidden actions, inconsistent patterns, cryptic error messages
• Near-miss: Beautiful but unusable (form over function)

Communication (Clear Writing):

• Positive: "Clear documentation"
• Negative: Jargon-heavy, assuming context, no examples, passive voice
• Near-miss: Technically accurate but incomprehensible to target audience

Strategy (Market Positioning):

• Positive: "Premium brand"
• Negative: Overpriced without differentiation, luxury signaling without substance
• Near-miss: High price without service quality to match

By Application

Teaching:

• Show common mistakes students make
• Provide near-miss solutions revealing misconceptions
• Identify "looks right but is wrong" patterns

Decision Criteria:

• Define disqualifiers (automatic rejection criteria)
• Show edge cases that almost pass
• Clarify ambiguous middle ground

Quality Control:

• Identify anti-patterns to avoid
• Show subtle defects that might pass inspection
• Define clear pass/fail boundaries

Guardrails

Near-Miss Selection:

• Near-misses must be genuinely close to positive examples
• Should reveal specific dimension that fails (not globally bad)
• Avoid trivial failures—focus on subtle distinctions

Contrast Quality:

• Explain why each negative example fails
• Show what dimension violates criteria
• Make contrasts instructive, not just lists

Completeness:

• Cover failure modes across key dimensions
• Don't cherry-pick—include hard-to-classify cases
• Show spectrum from clear pass to clear fail

Actionability:

• Translate insights into decision rules
• Provide guards/checks to prevent failures
• Make criteria operationally testable

Avoid:

• Strawman negatives (unrealistically bad examples)
• Negatives without explanation (show what's wrong and why)
• Missing the "close call" zone (all examples clearly pass or fail)

Quick Reference

Resources:

• resources/template.md - Structured format for anti-goals, near-misses, failure patterns
• resources/methodology.md - Advanced techniques (contrast matrices, boundary mapping, failure taxonomies)
• resources/evaluators/rubric_negative_contrastive_framing.json - Quality criteria

Output: negative-contrastive-framing.md with positive definition, anti-goals, near-misses with analysis, failure patterns, decision criteria

Success Criteria:

• Negative examples span boundary space (not just extremes)
• Near-misses are instructive close calls
• Contrasts clarify ambiguous criteria
• Failure patterns are actionable guards
• Decision criteria operationalized
• Score ≥ 3.5 on rubric

Quick Decisions:

• Clear anti-patterns? → Template only
• Subtle boundaries? → Use methodology for contrast matrices
• Teaching application? → Emphasize near-misses revealing misconceptions
• Quality control? → Focus on failure pattern taxonomy

Common Mistakes:

1. Only showing extreme negatives (not instructive near-misses)
2. Lists without analysis (not explaining why examples fail)
3. Cherry-picking easy cases (avoiding hard boundary calls)
4. Strawman negatives (unrealistically bad)
5. No operationalization (criteria remain fuzzy despite contrasts)

Key Insight: Negative examples are most valuable when they're almost positive—close calls that force articulation of subtle criteria invisible in positive definition alone.