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Record W2931799206

Youth Education for Advocacy: (Inter)national Dialogue on Advocacy for Human Rights, Equity, and Justice for First Nations

2019· article· en· W2931799206 on OpenAlexaffabout
Lynette Shultz, Anna Kirova, Thashika Pillay, Carrie Karsgaard, Dale Saddleback

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInjusticeEquity (law)Human rightsMainstreamPolitical scienceIndigenousCurriculumEconomic JusticeSociologyIndigenous rightsPublic relationsLaw
DOInot available

Abstract

fetched live from OpenAlex

For six months, youth from a diverse cross-section of schools across Canada worked on a curriculum project aimed at addressing issues of injustice and inequality facing First Nations children in Canada. Youth came together to collaborate, through the use of technology, to discuss the youths’ vision of justice for First Nations children as well as possible avenues of advocacy to ensure justice and equity. As part of a a decolonizing project that calls for “epistemic disobedience and delinking from the colonial matrix [of power] in order to open up decolonial options” (Mignolo, 2011, p. 9), this project holds potential to decenter mainstream notions of how to ensure justice and equity for First Nations children and simultaneously open new ways of taking up education for reconciliation that attempt to place Indigenous knowledges, traditions and cultures at the centre of our learning. This panel presentation will bring together youth, teachers, and researchers involved with the project to understand youth and advocacy from and the limits and possibilities of an online engagement around and for advocacy, human rights and equity for youth and teachers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.350
Teacher spread0.291 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

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