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Record W2116613790 · doi:10.33524/cjar.v14i1.71

EMPOWERING MARGINALIZED YOUTH: CURRICULUM, MEDIA STUDIES, AND CHARACTER DEVELOPMENT

2013· article· en· W2116613790 on OpenAlexaffvenueabout
Nicholas Ng-­A-­Fook, Linda Radford, Shenin Nadia Yazdanian, Tracy Norris

Bibliographic record

VenueThe Canadian Journal of Action Research · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsAlgonquin CollegeBishop's UniversityUniversity of Ottawa
Fundersnot available
KeywordsCurriculumPedagogyActive listeningAction researchSociologyCharacter developmentCurriculum developmentCharacter educationDigital mediaSocial mediaMathematics educationCharacter (mathematics)PsychologyPolitical scienceCommunication

Abstract

fetched live from OpenAlex

Students are bombarded daily with print, visual, and digital media. Whether it is on a billboard, listening to an iPod on the way to school, or text messaging a friend during class, youth culture is hardwired into these multiple forms of communication technologies. Nonetheless, the daily life and respective experiences of students are often still subordinated to the school curriculum. Our social action curriculum project, which targeted “at risk” youth at a vocational high school in the Ottawa region, attempted to disrupt this by integrating emergent digital technologies and differentiated instructional strategies into five Grade 10 courses over a span of two years. Devising what we call a “socio-culturally responsive media studies curriculum,” we addressed the following Ontario Character Development Initiatives: (1) Academic achievement; (2) Character development; (3) Citizenship development; and (4) Respect for diversity. But, what happens when social action researchers and teachers seek to institutionalize such taken-for-granted use of digital media within their design and implementation of the provincial curriculum and these character development initiatives? In response to this question, this paper will examine the curriculum we implemented with teachers and students in order to negotiate the four character development initiatives. As well, we examine how our curriculum research and the implemented program specifically created spaces for marginalized voices to be heard, and multiple literacies to flourish.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.279
GPT teacher head0.390
Teacher spread0.112 · 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

Citations4
Published2013
Admission routes3
Has abstractyes

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