La Contextualisation Des Banques de Ressources: Barrières et Clés
Bibliographic record
Abstract
When developing learning resource banks for telearning, it is important to recognize known barriers to the re-use of learning objects and some possible solutions. First, in order that the resources be reused, they must have been created in a modular fashion, separating the resources from their intended context, storing separately the documents and their use in scenarios, or in the case of executable code in autonomous segments. Next, in order to promote the gradual exploration, understanding, evaluation and choice of learning objects, it is important to link the resources not only with metadata, but also explicit documentation on the theoretical and practical aspects of their use, as well as any relevant evaluation material. Finally, if we really want these resource banks to hold this rich ancillary information, we need to build tools that support this contextualization and the activities of all the actors in the telelearning environment from conception, through development and use.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".