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Record W4293785500 · doi:10.5539/ass.v18n9p42

Exploration of the Motivation of Secondary Vocational Education Under the Background of “Diversion”—Based on a Case Study of Impoverished Junior High School Students in Northwest Rural Areas of China

2022· article· en· W4293785500 on OpenAlexvenueno aff
Cuicui Zhu

Bibliographic record

VenueAsian Social Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMedical and Agricultural Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationDisadvantagedPromotion (chess)PublicityChinaRural areaPsychologySociologyPedagogyMedical educationEconomic growthPolitical scienceBusinessMarketingMedicineEconomics

Abstract

fetched live from OpenAlex

The society generally regards the students who choose the secondary vocational school as the students who failed the high school entrance examination. Based on interviews and on-site observations of poor junior high school students and their parents in a rural junior high school in Northwest China, the paper summarizes the four major motivations for rural poor students to enter secondary vocational schools under the background of today’ s high school entrance examination diversion. The cost of educational choices and compromises, the influence of peer groups, and individual interests are motivated by school, family, peer, and individual motives, respectively. In view of the current fierce competition in education in China, the disparity of educational resources between regions and the embarrassed family background, the decision to enter secondary vocational school is the result of the interaction between the macro-structure and micro-situation of poor rural junior high school students. In conclusion, promotion to secondary vocational education is a reasonable choice for many rural families. Therefore, in order to meet the survival and development needs of the disadvantaged rural poor students and their families, and make vocational education truly a channel for their upward mobility, it is necessary to strengthen the publicity of vocational education, change the concept of discrimination against vocational education, and at the same time strive to improve vocational education.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.344
Teacher spread0.313 · 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
Published2022
Admission routes1
Has abstractyes

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