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Record W2955521095 · doi:10.17645/mac.v7i2.1817

Board Games as Interview Tools: Creating a Safe Space for Unaccompanied Refugee Children

2019· article· en· W2955521095 on OpenAlexfundno aff
Annamária Neag

Bibliographic record

VenueMedia and Communication · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
FundersYork University
KeywordsBespokeRefugeeSociologyVisual researchPhoto elicitationSpace (punctuation)Public relationsMedia studiesPolitical scienceComputer scienceVisual artsLaw

Abstract

fetched live from OpenAlex

Since the emergence of the new sociology of childhood in the late 1980s, there has been an increasing expectation to engage children actively and to take their views seriously throughout the research process. This is even more important when it comes to unaccompanied refugee children, whose voice is seldom heard. In this article the author builds upon her project of exploring unaccompanied refugee children’s lived media experiences and argues that—in order to have meaningful results and to create safe spaces for those who need it most—we need to search beyond traditional research tools. Specifically, she proposes to bring into research the concept of “play”. The article presents the use of bespoke, artisanal board games in cross-national interview settings with unaccompanied refugee children. It is argued that these creative tools can help in collecting diverse and rich data that can successfully complement traditional research 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 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)0.006
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0050.005
Open science0.0020.009
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.335
Teacher spread0.306 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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