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Record W3045360340 · doi:10.1177/1609406920938958

Documenting Syrian Refugee Children’s Memories: Methodological Insights and Further Questions

2020· article· en· W3045360340 on OpenAlexaffabout
Mehrunnisa Ali, Gina Gibran

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRefugeeNegotiationEthnic groupPrincipal (computer security)Syrian refugeesPsychologyDevelopmental psychologyPolitical scienceSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

Several scholars advocate for children’s experiences to be articulated by children themselves, and some have offered strategies on how to facilitate this. Yet there are hardly any studies that record children’s memories while they are children and offer methodological guidance on how to do so. None that we know of have recorded the unique memories of Syrian refugee children, possibly because of ethical, relational, and practical challenges of working with children considered especially vulnerable due to their age, ethnicity, and experiences as refugees. This article offers an account of how we engaged 13 Syrian refugee children (5–13 years old) in creating their autobiographies—based on memories of their lives in Syria, a transit country, and Canada—which they presented to other children in the study, in the presence of their parents, a school principal, and the researchers. In this article, we identify insights we gained by addressing issues raised by our Research Ethics Board; negotiating our roles and relationships with the children, their parents, and each other; and collecting data from the children in multiple forms. We also raise many questions, which we hope will engage other researchers in developing our collective expertise for recording understudied children’s memories.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.565
GPT teacher head0.625
Teacher spread0.059 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

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