Knowledge Organization Systems and Provenance: Experiences and Challenges
Bibliographic record
Abstract
ABSTRACT This panel is situated at the intersections of Knowledge Organization Systems (KOS) and provenance research. In this panel, we will share experiences and challenges in documenting the changes of a KOS The panelists will provide real‐world examples drawn from their research and practice. These examples range from KOSs used in LIS, such as the Dewey Decimal Classifications, Homosaurus, and Library of Congress Subject Headings; to extended information science research fields, such as digital preservation metadata standards, biodiversity taxonomies, and ontologies. Currently, the models and practices for documenting changes in KOSs fall short in supporting even simple provenance queries like “who made the change?”“what are the reasons for the change?”or“when was the change made?” The panelists will collectively discuss examples of queries we have faced, as well as the implications for provenance for KOS research and practice.This panel is sponsored by Classification & Metadata Research Special Interest Group (SIG‐CMR).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".