A New Guide to KF Modified: KF Classification Modified For Use In Canadian and Common Law Law Libraries
Bibliographic record
Abstract
The revised and updated edition of the Guide to KF Classification Modified for Use in Canadian Law Libraries written by Janet M. Moss of the Gerard V. La Forest Law Library at the University of New Brunswick in 2004 and published in 2006 as an Occasional Paper by the Canadian Association of Law Libraries/L'Association canadienne des bibliothèques de droit. This revision clarifies the use of KF Modified, incorporates the enhancements and improvements that have been applied to the KF Modified classification scheme during the intervening years, and contributes to the consistent use of this classification scheme in law libraries using or considering the use of KF Modified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.044 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".