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Record W3162531296 · doi:10.1080/00344087.2021.1917848

Changing Students’ Perceptions by Humanizing<i>Dati</i>Israelis through Comics

2021· article· en· W3162531296 on OpenAlexaff
Matt Reingold

Bibliographic record

VenueReligious Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsComicsJudaismSociologyPortraitSalience (neuroscience)Religious educationDiversity (politics)PerceptionQualitative researchPedagogyPsychologyGender studiesSocial psychologySocial scienceLawAnthropologyArtPolitical scienceVisual artsHistory

Abstract

fetched live from OpenAlex

A qualitative practitioner research study was conducted with 31 high school students studying religion contemporary Israeli society. The purpose of the study was to understand how using cartoons written and illustrated by the religious Jewish-Israeli settler Shay Charka challenged students to think about religion in Israeli society in new ways and whether introducing perspectives that were foreign to their North American Jewish education led to new ways of relating to and understanding Jewish-Israeli communities. Results of this small-scale study yielded that the comics were successful in introducing new ways of thinking about religion and in introducing a more complex portrait of Israeli society. As a pedagogical device, comics proved to be of salience and interest to the learners, which also led students to be motivated to study them. Students were particularly interested in the ways that Charka subverted their assumptions of gender in religious-Israeli communities and this specifically led to increased awareness of religious diversity in Israeli society.

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)0.003
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.002
Open science0.0010.004
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 head0.016
GPT teacher head0.348
Teacher spread0.332 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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