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Record W2989594744 · doi:10.22215/cjcr.v6i1.2159

“Can Disability Be Positive?” Reflecting on Children’s Rights and Disability through Shaking the Movers: A Youth-led Consultative, Collaborative, Participatory Model

2019· article· en· W2989594744 on OpenAlexaffvenueabout
Daniella Bendo

Bibliographic record

VenueCanadian Journal of Children s Rights / Revue canadienne des droits des enfants · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsCarleton University
Fundersnot available
KeywordsCitizen journalismPerspective (graphical)Collaborative modelParticipatory action researchIntellectual disabilityKey (lock)Resource (disambiguation)Disability studiesPsychologyPolitical scienceSociologyPedagogyPublic relationsGender studiesComputer scienceLawComputer security

Abstract

fetched live from OpenAlex

This article takes a reflective approach from the perspective of the National Coordinator of a youth-led consultative and collaborative model known as Shaking the Movers, developed by The Landon Pearson Resource Centre for the Study of Childhood and Children’s Rights at Carleton University in Ottawa, Ontario. It reflects on the model which was used to run a workshop that focused on children’s rights and disability to explore the guiding question: “Can Disability be Positive?” It reveals how the event unfolded, key messages from working with children and youth with disabilities, how the model worked or instances when it did not and key components that are helpful for other’s who may be interested in organizing a youth-led consultative and collaborative workshop with young people with disabilities. Ultimately, the paper explores the concepts of marginality and relationality and the ways these notions highlight how adult-centric views creep into the best laid efforts of adults who are aware of the strength of youth-led workshops.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.318
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes3
Has abstractyes

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