KBART Phase III: Unresolved questions
Bibliographic record
Abstract
During the “NISO update” session at the NISO Plus 2021 conference, which took place online due to the COVID-19 pandemic, members of the KBART (Knowledge Base and Related Tools) Standing Committee presented their plans and work toward KBART Phase III, a revision of the KBART Recommended Practice. In an interactive breakout session, they sought input from attendees on how KBART is being used and what new content types it should support. Presenters from the KBART Standing Committee were Noah Levin (Independent Professional), Stephanie Doellinger (OCLC, Inc.), Robert Heaton (Utah State University), and Andrée Rathemacher (University of Rhode Island). Assisting them in preparing the presentation were Jason Friedman (Canadian Research Knowledge Network), Sheri Meares (EBSCO Information Services), Benjamin Johnson (ProQuest), Elif Eryilmaz-Sigwarth (Springer Nature), and Nettie Lagace (NISO).
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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 | 0.175 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".