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Record W7010336990

Hezkuntza hezkidetzailea: kasu baten ikerketa

2016· dissertation· es· W7010336990 on OpenAlexaboutno aff

Bibliographic record

VenueAcademica-e (Universidad Pública de Navarra) · 2016
Typedissertation
Languagees
FieldSocial Sciences
TopicGender and Feminist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Power (physics)HierarchyInequalityQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

La desigualdad entre sexos es evidente en la sociedad de hoy en día, y por tanto tiene importancia en la educación. El sistema en el que vivimos podríamos definir como sexista, basada en una sociedad y cultura androcéntrica. Hechos como la violencia de género están vinculados con esta sociedad. Ante esta realidad, la necesidad de enfrentarse y buscar una alternativa es fundamental. Para ello, la educación se convierte en medio resolutorio: una de las bases del sistema educativo debería de ser la coeducación. De este modo, se lograría educar en igualdad, sin relación de dependencia del sistema sexo-genero, favoreciendo la igualdad entre sexos. Así, se desarrollarían todas las habilidades del alumnado y se terminarían con los estereotipos. Sin embargo, en este trabajo se investiga si en la escuela de hoy en día se garantiza este tipo de educación y se estudia si la comunidad de la escuela junto a la sociedad, produce desigualdad entre géneros o no, para ello analizando si niños y niñas de 6 años tienen roles y estereotipos de género interiorizados

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0480.015

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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