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Record W2165866921 · doi:10.20360/g2g016

(Re)Engendering Classroom Space: Teachers, Curriculum, Policy, and Boys’ Literacy

2011· article· en· W2165866921 on OpenAlexaffvenueabout
Jan Pennycook

Bibliographic record

VenueLanguage and Literacy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCurriculumContext (archaeology)PedagogyLiteracySpace (punctuation)SociologyResource (disambiguation)Work (physics)Mathematics educationPsychologyEngineeringGeography

Abstract

fetched live from OpenAlex

How teachers are engaging with a particular Ontario curriculum resource document, Me Read? No Way!, and the problem of boys’ literacy achievement in the context of a globalized neoliberal discourse of ‘failing boys’ has important implications for pedagogy and practice in the classroom. This investigation into teachers’ work adopts a feminist poststructural framework and uses critical discourse analysis to develop two case studies based on focus group interviews with a purposeful sampling of Intermediate level teachers. Not only are boys’ perceived needs and interests driving teacher choices in pedagogy and resource materials, but girls are perceived as not having any particular educational needs at all: boys will be boys and girls will be good. This investigation concludes that teacher professional knowledge must include a more developed understanding of how the social construction of gender is negotiated in the classroom.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.018
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.309
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 designObservational
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

Citations3
Published2011
Admission routes3
Has abstractyes

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