G.: Global vs. Community Metadata Standards: Empowering Users for Knowledge Exchange
Bibliographic record
Abstract
Abstract. The idea of knowledge sharing has strong roots in the education process. With the current development of the technology and moving learning material into the web environment it acquired a new dimension. Learning objects are the chunks of knowledge shared by e-learning community. Organizations and individuals are building repositories of learning objects and annotate them with metadata to describe their educational values and standardization efforts are on the way to provide a franca lingua for the educators. In this paper we describe the peer-to-peer infrastructure for sharing learning object we are building in Canada. The POOL projects builds on the three types of nodes: SPLASH is an freely downloadable application which allows individuals to create metadata and maintain their collection of learning objects, PONDs are bigger repositories of learning objects connected to the peer-to-peer network and POOL centrals increase the speed and breadth of the searches in the peer-to-peer network. The POOL project uses CanCore- a subset of the IMS metadata protocol- to describe learning objects. In the
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".