Interoperability models in digital libraries: an overview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it