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Record W3033409783 · doi:10.46303/jcsr.02.01.5

Building Bridges Instead of Walls: Engaging Young Children in Critical Literacy Read Alouds

2020· article· en· W3033409783 on OpenAlexaff
Cassie J. Brownell, Anam Rashid

Bibliographic record

VenueJournal of Curriculum Studies Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCritical literacySituatedEmpathyQualitative researchPoliticsLiteracySociologyCritical readingPsychologyPedagogyGender studiesPolitical scienceSocial psychologySocial scienceReading (process)Law

Abstract

fetched live from OpenAlex

Situated in the months after the 2016 United States presidential election, this qualitative case study illuminates third-grade children’s sense-making about the GOP Administration’s proposed border wall with Mexico. In light of these present-day politics, close analysis of how young children discuss social issues remains critical, particularly for social studies educators. Looking across fifteen book discussions, we zero in on three whole-class conversations about (im)migration beginning with initial read alouds through the final debrief wherein children conversed with a local university anthropologist about the clandestine migration of individuals across the U.S.’s southern border. During initial discussions, children in the Midwestern school demonstrated their frustration towards racist laws of the mid-1900s. Others responded with empathy or made personal connections to their own family heritage. In the findings, we note a clear progression in how children understood (im)migration issues as evidenced by how their questions and curiosities shifted in later lessons. We highlight how, when children are encouraged to engage with social topics, they can act as critical consumers and position themselves as politically active and engaged citizens.

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)0.005
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.298
GPT teacher head0.558
Teacher spread0.260 · 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.

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

Citations15
Published2020
Admission routes1
Has abstractyes

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