Interfaces to knowledge organization systems in Canadian digital library collections
Bibliographic record
Abstract
Purpose The purpose of this paper is to report an investigation into the ways in which Canadian digital library collections have incorporated knowledge organization systems into their search interfaces. Design/methodology/approach A combination of data‐gathering techniques was used. These were as follows: a review of the literature related to the application of knowledge organization systems, deep scanning of Canadian governmental and academic institutions web sites on the web, identify and contact researchers in the area of knowledge organization, and identify and contact people in the governmental organizations who are involved in knowledge organization and information management. Findings A total of 33 digital collections were identified that have made use of some type of knowledge organization system. Thesauri, subject heading lists and classification schemes were the widely used knowledge organization systems in the surveyed Canadian digital library collections. Research limitations/implications The target population for this research was limited to governmental and academic digital library collections. Practical implications An evaluation of the knowledge organization systems interfaces showed that searching, browsing and navigation facilities as well as bilingual features call for improvements. Originality/value This research contributes to the following areas: digital libraries, knowledge organization systems and services and search interface design.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".