Information Repositories and Learning Environments
Bibliographic record
Abstract
Information repositories are collections of digital information which can be built in several different ways and with different purposes. They can be collaborative and with a soft control of the contents and authority of the documents, as well as directedto the general public (Wikipedia is an example of this). But they can also have a high degree of control and be conceived in order to promote literacy and responsible learning, as well as directed to special groups of users like, for instance, school students. In the new learning environments built upon digital technologies, the need to promote quality information resources that can support formal and informal e- learning emerges as one of the greatest challenges that school libraries have to face. It is now time that school libraries, namely through their regional and national school library networks, start creating their own information repositories, oriented for school pupils and directed to their specific needs of information and learning. The creation ofthese repositories implies a huge work of collaboration between librarians, school teachers, pupils, families and other social agents that interact within the school community, which is, in itself, a way to promote cooperative learning and social responsibility between all members of such communities. In our presentation, we will discuss the bases and principles that are behind the construction of the proposed information repositories and learning platforms as well as the need for a constant dialogue between technical and content issues.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.041 | 0.053 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".