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Record W2915215940 · doi:10.1177/1468794119830076

Participatory training in disability and migration: mobilizing community capacities for advocacy

2019· article· en· W2915215940 on OpenAlexaffabout
Natalie Spagnuolo, Yahya El‐Lahib, Kaltrina Kusari

Bibliographic record

VenueQualitative Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of CalgaryYork University
Fundersnot available
KeywordsPraxisParticipatory action researchTransformative learningSociologyOppressionGeneral partnershipCitizen journalismTraining (meteorology)Public relationsGender studiesPedagogyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article offers methodological and theoretical reflections on a recent community-research partnership and participatory training program that was designed with the goal of improving the settlement experiences of migrants with disabilities living in Canada. Anchored in critical theoretical and anticolonial studies and offering intersectional perspectives on forms of oppression experienced by migrants with disabilities, our training program represents a collaborative form of knowledge production with transformative potential for front-line workers and organizers. In this article, we begin the reflective process by unpacking our approach to participatory training, explicating our theoretical assumptions, and linking our values and theories to praxis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0270.056
Scholarly communication0.0100.007
Open science0.0030.026
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.632
GPT teacher head0.625
Teacher spread0.008 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2019
Admission routes2
Has abstractyes

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