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Record W4406289390 · doi:10.25071/28169344.114

Us and Them

2025· article· en· W4406289390 on OpenAlexaff
Shabnam Sukhdev

Bibliographic record

VenueYU-WRITE Journal of Graduate Student Research in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsYork University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper delves into the personal teaching journey of the author, focusing on the challenges and transformations encountered while teaching courses in Media Arts. It aims to explore strategies to facilitate active student participation and promote transformative learning experiences, particularly within a diverse classroom environment. Drawing from the educational philosophy of Paulo Freire, particularly his emphasis on dialogic education and the role of students as active participants in the learning process, this study is grounded in constructivist and social constructivist theories. It also incorporates concepts from performance studies, including Augusto Boal's "Theater of the Oppressed," to inform teaching methods that encourage critical engagement and creative expression. The author's teaching approach is examined through a qualitative lens, utilizing reflective analysis and classroom observations to document experiences and outcomes. Interviews with students and thematic analysis of classroom activities provide insight into the effectiveness of strategies aimed at fostering openness, inclusivity, and critical thinking. Through innovative teaching methods, such as project-based assessments and creative exercises, students were empowered to explore diverse perspectives and engage in meaningful dialogue. These approaches facilitated deeper learning and challenged students to confront biases and societal norms, ultimately leading to transformative growth and increased empathy.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.345
GPT teacher head0.590
Teacher spread0.245 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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