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Record W4411418724 · doi:10.1080/13676261.2025.2518955

Beyond innocence: the power of young people’s stories in resisting class-based inequality

2025· article· en· W4411418724 on OpenAlexaff
Cameron Greensmith, Adam Davies, Sara Z. Evans, Jalyn Lankford

Bibliographic record

VenueJournal of Youth Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInnocenceInequalityPower (physics)Class (philosophy)SociologySocial inequalitySocial classGender studiesPsychologyPolitical scienceEpistemologyPsychoanalysisMathematicsPhilosophyLawPhysics

Abstract

fetched live from OpenAlex

This study examines the FRONTLINE documentary Growing Up Poor in America, which sheds light on the stories of three young people from rural and suburban Ohio during the COVID-19 pandemic, emphasizing the harsh realities of class-based inequalities. Using a storytelling approach, this article aims to amplify the voices of these marginalized young people, challenging dominant developmental paradigms that often overlook their agency and understanding of systemic inequalities. The study explores how storytelling serves as a transformative tool to foster critical consciousness, inspire empathy, and encourage transformation. By prioritizing storytelling grounded in personal encounters, our approach not only confronts entrenched inequalities but also empowers marginalized young people by amplifying their narratives. We advocate for the transformative potential of storytelling to ignite meaningful dialogue, inspire action, and ultimately dismantle the barriers that perpetuate class-based disparities and other intersecting inequalities in the lives of young people.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.064
GPT teacher head0.387
Teacher spread0.323 · 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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