The interoperability of learning object repositories and services
Bibliographic record
Abstract
Interoperability is one of the main issues in creating a networked system of repositories. The eduSource project in its holisticapproach to building a network of learning object repositories in Canada is implementing an open network for learning services. Itsopenness is supported by a communication protocol called theeduSource Communications Layer (ECL) which closely implements the IMS Digital Repository Interoperability (DRI)specification and architecture. The ECL in conjunction withconnection middleware enables any service providers to join thenetwork. EduSource is open to external initiatives as it explicitlysupports an extensible bridging mechanism between eduSource and other major initiatives. This paper discusses interoperability in general and then focuses on the design of ECL as animplementation of IMS DRI with supporting infrastructure andmiddleware. The eduSource implementation is in the mature stateof its development as being deployed in different settings withdifferent partners. Two applications used in evaluating ourapproach are described: a gateway for connecting betweeneduSource and the NSDL initiative, and a federated searchconnecting eduSource, EdNA and SMETE.
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.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".