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CREENCIAS DE PROFESORAS DE EDUCACIÓN BÁSICA ACERCA DE LA EDUCACIÓN EN CIENCIAS Y GÉNERO: UN ESTUDIO DE CASO AL ENSEÑAR CIENCIAS NATURALES EN LA ESCUELA

2020· article· es· W3108116265 on OpenAlexaff
Giselle Francis Melo Letelier, Carolina Martínez-Galaz, Johanna Patricia Camacho González

Bibliographic record

VenuePerspectiva educacional · 2020
Typearticle
Languagees
FieldSocial Sciences
TopicSocial Skills and Education
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics

Abstract

fetched live from OpenAlex

Esta investigación exploratoria buscó comprender las creencias de dos profesoras de Educación Básica sobre la relación género-enseñanza de las ciencias. Mediante un estudio de caso, se analizaron entrevistas y observaciones de clases bajo el método de comparación. Las creencias de ambas participantes son similares. Sus creencias sobre la enseñanza de las ciencias son coincidentes con los objetivos de la Didáctica y para ambas el respeto es fundamental en la vinculación con sus estudiantes. Ambas tienen creencias binarias sobre el género, lo que se expresa también en sus creencias sobre las ciencias. Los constructos sociales permean su quehacer en el aula mostrando diferencias entre lo que creen hacer y lo que hacen. Durante los plenarios se observa una interacción, retroalimentación y designación de roles diferenciadas. Finalmente, se identifican modelos de género, posibilidades para tratar la perspectiva de género y desafíos para la investigación en el área.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.015
GPT teacher head0.336
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations7
Published2020
Admission routes1
Has abstractyes

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