Interoperability models in digital libraries: an overview
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an overview of the existing interoperability models in digital libraries and to introduce related projects in each model. Design/methodology/approach The study starts from searching various databases with a combination of important keywords in the field, such as interoperability, digital library, meta‐searching and cross‐searching. The study follows up with describing related digital library projects in the field of technical interoperability. The projects are described under three main categories, Federated, Harvesting and Gathering. Findings The study shows that most of the studied projects are located in the USA and also most of the digital library projects use OAI protocol and the harvesting model in order to be technically interoperable. Also, the results of the study showed that the projects mostly paid attention to metadata interoperability and only a few mentioned full‐text interoperability issues. Originality/value The paper makes an original contribution of exploring an area (interoperability models in digital libraries), that is at the forefront of discussion in libraries worldwide.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".