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Record W7133023858

Teachers' Beliefs About Gender Differences in Single-Sex Classrooms

2018· dissertation· W7133023858 on OpenAlexaffabout
Linda Hanceroglu

Bibliographic record

VenueTSpace · 2018
Typedissertation
Language
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsThe Wilson Centre
Fundersnot available
KeywordsSalience (neuroscience)Qualitative researchDoing genderGender biasGender equalityGender discriminationGender identity
DOInot available

Abstract

fetched live from OpenAlex

Some argue that girls’ and boys’ education should be based on biological differences between learners, while other studies argue that sex-based instruction problematically dismisses or ignores issues of gender. Using a qualitative design, this study addresses two main research questions concerning teachers’ beliefs about the salience of gender in their single-sex classrooms, and how teachers’ beliefs about gender shape their pedagogy. The 6 participating teachers were selected randomly from private, single-sex schools in the Greater Toronto Area. The results show teachers did not take a firm stance on “gender exploitive” (i.e., working with dominant gender constructions and stereotypes) or “gender transformative” (i.e., working against and to challenge dominant gender constructions and stereotypes) pedagogies and shared experiences that both reinforced and challenged gender norms. Conclusions drawn support the need for further professional development to build teacher capacity to effectively respond and address gender and gender issues in their classrooms.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.009
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.106
GPT teacher head0.395
Teacher spread0.289 · 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
Published2018
Admission routes2
Has abstractyes

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