MétaCan
Menu
Back to cohort
Record W4386595113 · doi:10.1080/02568543.2023.2248224

A Hermeneutic Phenomenological Inquiry: Risk and Protective Factors for the Left-Behind Children in Shaanxi Province, China, With Gender Comparison

2023· article· en· W4386595113 on OpenAlexaff
Lin Ge

Bibliographic record

VenueJournal of Research in Childhood Education · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsChinaPsychologyDevelopmental psychologyLeft behindPolitical sciencePsychotherapistMental health

Abstract

fetched live from OpenAlex

This research presents the lived experience of left-behind children living in Shaanxi province of China through the lens of resilience theory. A hermeneutic phenomenological inquiry is employed to identify inner and outer risk factors faced by left-behind children at the primary school level and explore their protective factors (internal assets and external resources). This study attempts to determine how these protective factors can moderate or reduce the repercussions of risk exposure on the children’s development. Unstructured observations, including on-site observations of the classroom, life, and interpersonal relationships, were conducted in 2018 and remote classroom observations were completed in 2020 due to the pandemic. There were 51 students (including 28 left-behind children) observed in total. Fourteen 9- to 13-year-old left-behind children, eight guardians, and six teachers participated in interviews. Remote pre-interview activities were specifically employed in 2020. The findings indicate this group’s internalized and externalized problems, and the internal assets and external resources. This study would potentially identify viable targets for interventions and socially inclusive education. Resilience-based interventions in policies and practices are recommended to intervene in the trajectory from risk exposure to associated negative effects.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.114
GPT teacher head0.446
Teacher spread0.332 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research in Childhood EducationSame topicMigration, Health and TraumaFrench-language works237,207