Repository navigation
Loop 76: Negative-Result Informativeness Guide (F6) - #761
Merged
Merged
Conversation
Add comprehensive guide defining informative vs non-informative negative results with a 7-dimension framework, 14 annotated examples across all 6 reason categories, before/after transformation pairs, and CLI classifier. - docs/evidence/NEGATIVE_RESULT_INFORMATIVENESS_GUIDE.md - scripts/classify_negative_result_informativeness.py - examples/negative_result_entry_example.json - tests/evidence/test_negative_result_informativeness.py (37 tests) - Makefile target: classify-negative-informativeness - Update METRICS_CURRENT.md, ROADMAP.md, PROJECT_INDEX.md - Bump test baseline to 2796 v0.5.77 — Loop 76: Phase F F6
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Change
Add comprehensive negative-result informativeness guide and CLI classifier (Phase F, F6).
Why
Negative results are only useful if they are informative. The guide defines a 7-dimension framework (identity, context, specificity, actionability, verifiability, structured metadata, interpretation) and provides 14 annotated examples across all 6 reason categories, plus before/after transformation pairs. The CLI classifier makes the classification machine-checkable.
Evidence
make classify-negative-informativenessclassifies the example entry as INFORMATIVE (6.25/7.0)make agent-checkpassesSafety impact
None. All outputs are computational classifications of documentation quality, not biological claims.
Proof level
Informational only. The guide and classifier evaluate documentation completeness, not scientific correctness.
Limitations
Human review needed
No.