Closing the Interoperability Gap: Connecting Open Service Interfaces with Digital Repository Interoperability
Bibliographic record
Abstract
Interoperability between e-learning systems and repositories is one of the hottest topics in e-learning community. With an availability of standards and specification for the single learning objects, courses and related learning artifacts the technical focus of the e-learning community has shifted towards the interoperability of between different learning systems and learning systems and other sources of digital objects such as digital libraries. In this paper we start with a review of the interoperability initiatives. Next, we describe eduSource’s ECL and OKI’s OSIDs: two approaches to interoperability and highlight their strengths and how they complement each other. Finally, we describe our present effort in merging the two approaches together with first results and observations from the implementation of the ECL/OKI connector within the scope of LionShare project.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.041 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".