Bibliographic record
Abstract
The “Learning Commons” is an innovative library concept developed and applied in the USA in the 1990’s. The main goal of this new library concept is to stimulate users to learn and experiment on their own, or as a team, with the help of many different tools. The Learning Commons is also a place where users can find information as in a traditional library, using books and online resources for example, but also a space where users can develop different skills, such as problem-solving or critical thinking, catalysed through the use of new technologies and even recycled materials. In some sense, this way of learning resembles the Italian Reggio method and Piaget theories. This way of learning is possible because the Learning Commons can be adapted and changed depending on user needs and interests. Today, it is a library model used by many universities and schools across North America, whereas in Italy we have few cases that are close to the Learning Commons philosophy. Here I analyse this new approach, and describe my personal experience as a Learning Commons librarian in a Canadian school, in terms of environment, users and the librarian’s job, compared with my previous experience in a traditional Public Library.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".