DEFINICI?N, DESCRIPCI?N Y ESTUDIO DE LOS SIMULADORES EN SOFTWARE LIBRE UTILIZADOS PARA EL APRENDIZAJE DE LA F?SICA
Bibliographic record
Abstract
This paper identity the state of the development of simulations in free software for physics and to development a repository prototype to register educational resources in Internet, facilitating the access to the community.Their results allow establishing the state of the simulation in software free with educational purpose; for software type, of language programming, of license, of operating system and area of knowledge, as area pilot it was the physics.It allowed to identify projects of use o simulators in it usa, Canada, Argentina, Spain, Portugal, France, Italy, Germany, England, Japan, China, Taiwan, Israel and Colombia, in 50 projects and already of 2097 resources.It was define the methodological model for the use of simulators in the education. The methodological model is sustained in the active learning (cf. Huber, 1997), the learning based on problems (Barrows, 2008), the incremental development of abilities and the support of the Information Technology (it), in order to make use of this software in the education.A test pilot of use of simulators was made, this it allowed to identify: advantages and disadvantages of the use of simulators. These educational resources offer alternative of access economic and excellent quality to improve the educational processes
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".