Condiciones, Procesos y Circunstancias que Permiten Avanzar Hacia la Inclusión Educativa: Retomando las Aportaciones de la Experiencia Canadiense
Bibliographic record
Abstract
Este artículo no pretende describir la experiencia canadiense como una muestra o compendio de “buenas prácticas” o modelos a seguir, sino que trata de sustraer algunos aspectos clave que podemos retomar para reflexionar sobre la situación de la educación inclusiva en cada contexto o país. Conocer los factores clave que han posibilitado el avance hacia la inclusión en Canadá, permitirá formular nuevas preguntas sobre la forma de organizar una educación más inclusiva.Las percepciones, opiniones y experiencias de los profesionales entrevistados se integran en el texto en forma de breves relatos, así como las narraciones que ilustran situaciones, hechos y contextos observados.Las valoraciones de la autora sobre la experiencia canadiense se articulan alrededor de factores críticos o condiciones para el cambio abordados desde una perspectiva sistémica, de manera que el trabajo se estructura en torno a cuatro niveles en los que se deberían tomar decisiones para proponer, de forma coherente y convergente, políticas y prácticas inclusivas. Descriptores: Inclusión educativa, factores clave, mejora educativa, políticas educativas. Necessary Circunstances, Processes and Conditions to Move Towards Educative Inclusion: The Canada ExperienceThe purpose of this paper is not to describe the Canadian experience as a sample or compendium of “good practices” or patterns to be followed, but to extract some key elements that can be taken up again in order to reflect on the situation of inclusive education in different countries or contexts. Knowing the key factors that allowed progress towards inclusion in Canada will allow us to raise new questions about the way to organise more inclusive education.The perceptions, opinions and experiences of the interviewed professional are included in the text as brief statements and accounts that illustrate the situations, facts and contexts observed. The author’s points of view about the Canadian experience revolve around critical factors or conditions for change, which are analysed from a systemic approach.This paper is structured in four levels that should allow decisions to be made for the purpose of proposing inclusive policies and practices in a coherent and convergent way. Keywords: Inclusive education, key factors, Canada, school improvement, educational policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".