Los ambientes de aprendizajes modernos: un componente pertinente para favorecer los procesos de inclusión en el CDI Fe y Alegría Madre Alberta de la ciudad Santiago de Cali.
Bibliographic record
Abstract
The denied education to doubt is an one belonging to the tools arranged to the human being to transform and to change all that meets to his around. And in order that the aforementioned transformations take place to end, it is necessary to do use of some facilitators at the educational institutions that the persons in charge to propose and to carry to end changes that match above and beyond simple improvements and in its step will be make new roads to attain goals jointly. To begin with this journey is important to understand that they form these new roads in innovative proposals than, with the adequate use theirs become in the propitious environments to develop the processes of inclusion, it is the same way that in the present I articulate of reflection will examine him how the environments of modern learning’s are a pertinent component to favor the processes of inclusion in the CDI Fe y Alegría Madre Alberta of Santiago de Cali City. For it, one will have in account the invention like an assertive bet to transform what's existent; The inclusion taken care of from different policies and programs that they watch over to guarantee the fulfillment of some conditions and contexts made suitable, that they establish measures and tools in behalf of quality and equity.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".