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Record W4327967413 · doi:10.54691/bcpbm.v41i.4434

Exploring Left Behind Children in China

2023· article· en· W4327967413 on OpenAlex
Yifei Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBCP Business & Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChinaUrbanizationLeft behindRural areaEconomic growthDistribution (mathematics)Resource (disambiguation)GeographySociologySocioeconomicsPsychologyPolitical scienceMental health

Abstract

fetched live from OpenAlex

With the rapid development of urbanization, more and more rural labourers decide to move out to the cities. But a large number of families of these out-migrating labourers are still in rural areas, so that their children are growing up with a serious lack of parental supervision and companionship. The education and psychological aspects of left-behind children have also become a major problem that needs to be taken seriously by society. This article reviews the previous studies of left-behind children in China, which aims to analyze the impact of Chinese children whose parents are working outside the home to urban cities in Rural communities and provides corresponding solutions. This article also explores how the restriction of resource distribution to migrant workers influences their children’s education status. This article’s major focus is also the claim that children’s educational development is negatively impacted by a lack of parental connection and monitoring.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.272
Teacher spread0.229 · 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