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Record W4401031747 · doi:10.1177/00812463241266337

Resilience to structural violence: an exploration of the multisystemic resources that enable youth hope

2024· article· en· W4401031747 on OpenAlexfundno aff
Bongiwe Ncube, Linda Theron, Sadiyya Haffejee

Bibliographic record

VenueSouth African Journal of Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsThematic analysisPsychological resiliencePsychologyNarrativeFace (sociological concept)Resilience (materials science)Social psychologySample (material)FaithQualitative researchDevelopmental psychologySociologySocial science

Abstract

fetched live from OpenAlex

Youth in structurally violent environments emphasise hope when explaining their resilience. Even so, the multisystemic resources that enable hope (also over time) are relatively underreported for African young people. In response to this gap, we report a qualitative study that identified the hope-enabling resources that contributed to the resilience of two samples of African youth aged 15–24 and living in the township of eMbalenhle, South Africa. Using Draw-Write-Talk methodology, the 2017 sample ( n = 30; M age = 18.6; 56% male youth) and 2018 sample ( n = 7; M age = 18.4; 85% female youth) generated visual and narrative data of their experiences of hope enablers. A thematic analysis showed that a multisystemic mix of contextually relevant resources typically explained youth capacity for hope in the face of structural violence. Four resources informed this mix: personal strengths, faith-based beliefs, positive personal relationships, and tangible sources of inspiration. This contextually relevant mix, and its putative durability over time, has implications for how psychologists and policymakers support youth in structurally violent contexts to be hopefully resilient.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.335
Teacher spread0.293 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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