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Record W4248640447 · doi:10.32920/ryerson.14640270

Refugee Youth And Migration: Using Arts-Informed Research To Understand Changes In Their Roles And Responsibilities

2021· preprint· en· W4248640447 on OpenAlexaboutno aff
Stella Abiyo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeFocus groupThe artsContext (archaeology)Participant observationSociologyQualitative researchInterpretation (philosophy)Gender studiesPsychologyPedagogyPublic relationsPolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

This article presents the findings from a community-based qualitative study that utilized an arts-informed method to understand the changes in refugee youth's roles and responsibilities in the family within the (re)settlement context in Canada. The study involved 57 newcomer youths from Afghan, Karen, or Sudanese communities in Toronto, who had come to Canada as refugees. The data collection method embedded a drawing activity within focus group discussions. We present these drawings, as well as explanations and discussions to capture the complexities of their experiences. The data analysis involved 1. reflective dialogue between each participant and her/his own drawing; 2. group dialogue, reflection, and elaboration on meanings in the drawings; and 3. the research team's reflective dialogue. The findings revealed that the youths' roles and responsibilities have both changed and increased following migration, often involving interpretation and translation, and providing financial and emotional support to their family members, in addition to engaging in household chores and educational pursuits. Use of drawings as a data generation method enriched the findings of focus group discussions, and vice versa in a number of ways. We also present implications for future research involving arts-informed methods.

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)0.017
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.874
GPT teacher head0.682
Teacher spread0.192 · 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
Published2021
Admission routes1
Has abstractyes

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