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Record W4387638894 · doi:10.52589/bjeldp-ibqzurpl

Unveiling the Silent Suffering: Exploring the Lived Experience of Girl Child Marriage Victims and its Impact on their Education

2023· article· en· W4387638894 on OpenAlexaff
Ambrose Kombat, Amanyi C.K., Asigri V.N., Stephen Atepor, Adugbire J.A., Adayira V.W.

Bibliographic record

VenueBritish Journal of Education Learning and Development Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGirlThematic analysisPsychologyPsychological interventionDevelopmental psychologyQualitative researchNarrativeGender studiesSociologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

This qualitative study aimed to explore the lived experiences of victims of girl child marriage, an issue that persistently affects countless girls worldwide, and its impact on the victim’s education. Using a phenomenological approach, the study delves into the narratives of girls who have been married off at an early age, examining their perspectives on the impact of marriage on their education, personal development, and overall well-being emotions. Through in-depth interviews and thematic analysis, the study reveals the multifaceted nature of girl-child marriage and its profound impact on the lives of its victims. Girl child marriage victims experienced emotional and psychological distress, socio-economic and health-related challenges, as well as a disruption in their education. By giving voice to the experiences of these girls, this research contributes to a deeper understanding of the intricacies surrounding girl-child marriage and underscores the urgent need for targeted interventions and policy changes.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.014
Scholarly communication0.0050.005
Open science0.0020.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.398
Teacher spread0.335 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Education Learning and Development PsychologySame topicIntimate Partner and Family ViolenceFrench-language works237,207