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Record W2391535882

School District System: A Realistic Way to Crack the Problem of Compulsory Education School Selection for Students in China

2016· article· en· W2391535882 on OpenAlexvenueno aff
Guoliang Guo

Bibliographic record

VenueCanadian social science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
Fundersnot available
KeywordsConnotationChinaPromotion (chess)Compulsory educationMathematics educationSchool districtValue (mathematics)SociologyResource (disambiguation)Political sciencePedagogyComputer sciencePsychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Looking through “nearest school entrance” policy evolution in China for nearly 30 years, the inherent logic of its value pursuit should act as receive compulsory education equally, whereas neighborhood entrance is just a path to realize the equality. However, under the circumstance of huge gap between different schools in China, neighborhood entrance policy will not necessarily lead to education fair, but only a sub-optimal choice. School district system is a bold breakthrough of institutionalized “nearest school entrance” policy. Through building rational “students flow” system, It meets the individualized education demand of parents, and ensure education equality. Therefore, school district division standard should transform from adhering to convenient education administrative to the free, flexible and convenient choices for students to choose school. School district division should also stick to the coordination of three principles: “nearest school entering, student needs and school developments”. The policy should also be rooted in multiple school division on the basis of relevantly balanced school resource, appropriate promotion of students flow, exploration of scientific district management mode, changing from the “gap cooperation” to “difference cooperation”, so as to build a connotation development path.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.345
Teacher spread0.328 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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