Category
Spike
Question
What evidence and review process should determine labels and static scores while preserving a deterministic synthetic dataset?
Context
Labels are synthetic and notes-based; scores are static, while research plans accuracy and false-positive measurements.
Why This Matters
Evaluation is only meaningful when labels and scores are explainable, repeatable, and protected from silent bias.
Areas to Investigate
Expert review, adversarial clean controls, score calibration, reviewer agreement, provenance, exceptions, and synthetic scenario generation.
Evaluation Criteria
Auditability, reproducibility, false-positive usefulness, reviewer effort, privacy, and dataset balance.
Expected Deliverables
Governance proposal, provenance design, review checklist, calibration method, and example reviewed change.
Acceptance Criteria
Follow-Up Opportunities
May lead to provenance fields and locked evaluation manifests.
Cross-Repository Impact
Testkit and research directly; adapter and extension consume the result.
Complexity
Spike
Impact
High — safeguards product measurements.
Suggested Labels
spike, area: scoring, area: fixtures, area: evaluation
Category
Spike
Question
What evidence and review process should determine labels and static scores while preserving a deterministic synthetic dataset?
Context
Labels are synthetic and notes-based; scores are static, while research plans accuracy and false-positive measurements.
Why This Matters
Evaluation is only meaningful when labels and scores are explainable, repeatable, and protected from silent bias.
Areas to Investigate
Expert review, adversarial clean controls, score calibration, reviewer agreement, provenance, exceptions, and synthetic scenario generation.
Evaluation Criteria
Auditability, reproducibility, false-positive usefulness, reviewer effort, privacy, and dataset balance.
Expected Deliverables
Governance proposal, provenance design, review checklist, calibration method, and example reviewed change.
Acceptance Criteria
Follow-Up Opportunities
May lead to provenance fields and locked evaluation manifests.
Cross-Repository Impact
Testkit and research directly; adapter and extension consume the result.
Complexity
Spike
Impact
High — safeguards product measurements.
Suggested Labels
spike,area: scoring,area: fixtures,area: evaluation