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Boys in Religious Education – a difficult relationship?! Considering perspectives of boys in a gender-balanced pedagogy of diversity

2018· article· en· W2901984356 on OpenAlexvenueno aff
Thorsten Knauth

Bibliographic record

VenueEncounters in Theory and History of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsDominance (genetics)SilenceGender studiesPerspective (graphical)CriticismFeminismReligious educationSociologyDiversity (politics)Feminist philosophyPsychologyGender biasPedagogySocial psychologyAestheticsPhilosophyLiteratureArt

Abstract

fetched live from OpenAlex

Pedagogy of Religion has been, for a long time, pedagogy of skipped gender difference. In a wrong generalization, students were talked about sweepingly. However, Religious Education (RE) never was neutral to gender; when it pretended to be so it actually supported male dominance. Crucial for the pioneer work of feminist theology was to reveal the gender veil (Pissarek-Hudelist, 1981, p. 47-71): The silence about the significance of gender supported an unreflected acceptance of maleness as the standard. Feminist religious pedagogy tried to overcome androcentrism, the dominance of male perspectives in religious education in contents, aims, and interactions (Jakobs 1994, p. 97-106; Kohler-Spiegel, 1995, p. 204-211; Pithan, 1993, p. 421-435). Outcomes of research in Feminist Theology and feminist approaches to Religious Education have led to a necessary emphasis on structural discrimination of girls in school education and contributed to increasing awareness of gender bias in RE-approaches. The feminist perspective was bitterly needed and still is. But while the way of looking at girls, their life situations, needs, and gender-related educational approaches could become more differentiating, the perspective on boys has remained strangely bleak, being stuck in the criticism of male dominance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.083
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.341
Teacher spread0.311 · 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 teacher head, 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 routes1
Has abstractyes

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