Modelación matemática y uso de tecnología bajo una perspectiva STEM
Bibliographic record
Abstract
El proyecto STEM (Science, Technology, Engineering, Mathematics), nació a finales del siglo XX, cuyo principal objetivo es la unificación de las ciencias. El proyecto es muy ambicioso ya que no solo se realiza un cambio curricular en el área de matemáticas, sino en todas las áreas de la ciencia; solicitando al profesor ligado a este cambio, una cultura amplia desde el punto de vista científico. De acuerdo con English (2015), en Australia se cuenta con varias generaciones de ingenieros STEM; sin embargo, desde su punto de vista, ella considera que en la formación del nuevo ingeniero, la matemática juega un papel secundario, exclusivamente de corte utilitario. Con esto ella quiere decir que el aprendizaje no es conceptual, sino procedimental. Así, si la modelación matemática es uno de los aspectos más fuertes en la formación de conceptos, ella juega un papel secundario en el proyecto STEM y para los didactas de la matemática no es adecuado.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".