Interoperability Frameworks for Learning Object Repositories
Bibliographic record
Abstract
Learning object repositories hold the digital resources that make on-line instruction possible. Whether held by individuals, learning communities, or purveyors of knowledge artifacts, the reusability and hence the potential market for e-learning objects depends on the extent to which the objects can be found, selected for appropriateness, and retrieved for use in a new instructional context. This presentation outlines the current efforts of eduSource Canada, a Canadian consortium building a national interoperability framework for both academic and industrial contexts. As eduSource strives to unite both peer-to-peer and web services models, the mechanisms for interoperability at the transactional and semantic levels are described in some detail. Key to the solutions proposed are the ECL or eduSource Communications Layer, an open protocol to enable search, gather and retrieval within the eduSource community and gateways which extend this functionality to other learning object repository networks. 1
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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".