Knowledge organisation systems in North American digital library collections
Bibliographic record
Abstract
Purpose The purpose of this paper is to report an investigation into the types of knowledge organisation systems (KOSs) utilised in North American digital library collections. Design/methodology/approach The paper identifies, analyses and deep scans online North American hosted digital libraries. It reviews the literature related to the application of KOSs on the web, identifies widely used KOSs and tools and reviews the literature related to collaborative collections on the web. Findings A total of 269 North American digital library collections were examined. The Library of Congress Subject Headings is the most widely used subject representation tool, followed by domain‐specific thesauri, 113 digital library collections make use of locally developed taxonomies. A few collections use the Dewy Decimal Classification and alphabetical indexes. Research limitations/implications This research was limited to North American digital library collections. Practical implications The findings show the popular KOSs used in digital library collections. It also shows the organisational contexts of the examined digital library collections. Originality/value This research contributes to the areas of digital libraries and to the application of KOSs and services for subject representation and access.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".