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Record W2060905837 · doi:10.1504/ijlt.2005.007151

Linking learning object repositories

2005· article· en· W2060905837 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Learning Technology · 2005
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsSimon Fraser UniversityBritish Columbia Institute of Technology
FundersNatural Sciences and Engineering Research Council of CanadaMinisterstvo Školství, Mládeže a TělovýchovyCanarieAndrew W. Mellon Foundation
KeywordsComputer scienceObject (grammar)World Wide WebInformation retrievalArtificial intelligence

Abstract

fetched live from OpenAlex

Learning object repositories hold the digital media resources for online learning. Success in the distribution and re-use of learning objects depends on how well the objects can be found, screened, and retrieved for use in a new instructional context. The eduSource Communications Layer (ECL) was developed as the interoperability component for eduSource Canada, a Canadian consortium for a national learning objects infrastructure. The ECL application enables searching across both peer-to-peer and web services architectures. Mechanisms for interoperability at the transactional and semantic levels are described in some detail. Key to the ECL is an open protocol to enable search, gather and retrieval within the eduSource community and gateways to extend this functionality to other learning object repository networks and digital libraries. Within six months of its release the ECL demonstrated federated searches with various learning object repositories in Canada, Australia, the United States, the United Kingdom, and the European SchoolNet.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.289
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it