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Record W2883528476 · doi:10.1111/cag.12482

Spatial equity in accessing secondary education: Evidence from a gravity‐based model

2018· article· en· W2883528476 on OpenAlexaffvenue
Changdong Ye, Yushu Zhu, Jiangxue Yang, Qiang Fu

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of British ColumbiaAsia Pacific Foundation of Canada
FundersNational Natural Science Foundation of China
KeywordsEquity (law)Neighbourhood (mathematics)ChinaGeographySocial equalitySalientInequalitySpatial inequalityEconomic growthEducational equityRegional sciencePolitical sciencePsychologyPedagogyEconomics

Abstract

fetched live from OpenAlex

Whereas education inequality has attracted wide scholarly attention, there has been little attention paid to the spatial patterns of schools or, more specifically, the spatial equity of secondary schools. This study investigates the spatial patterns of secondary schools (regular vs. key) in Guangzhou, China and disparities in school accessibility among different social groups at the neighbourhood level. We use a two‐step floating catchment area method to measure school accessibility. Our results demonstrate strong spatial disparities in secondary school accessibility in Guangzhou and further underscore significant associations between the access to secondary schools, especially key schools, and neighbourhood characteristics. This study helps to document salient spatial inequity in China's current education system and suggests that efforts should be made to reduce the country's spatial inequity in secondary 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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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 designSimulation or modeling
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

Citations15
Published2018
Admission routes2
Has abstractyes

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