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Record W2203810049 · doi:10.3968/7398

Study on the Distribution of Compulsory School Teacher’s Resources in Guizhou

2015· article· en· W2203810049 on OpenAlexvenueno aff
Yali Zhang

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadCompulsory educationQuality (philosophy)Mathematics educationDistribution (mathematics)School teachersPsychologyPedagogyPolitical scienceSociologyMedical educationMedicineMathematicsEconomicsManagementPhysics

Abstract

fetched live from OpenAlex

The student-teacher ratio of compulsory schools in Guizhou has declined. The student-teacher ratio of junior middle schools is higher than that of primary schools. The student-teacher ratio of urban schools is the highest of all. The teachers’ educational level of compulsory schools in Guizhou is higher than before. The structure of teachers’ professional titles is unreasonable in that the proportion of teachers with high professional titles is too low. The workload of teachers is too heavy to meet the demand of diversified running of schools in Guizhou. To improve the quantity and quality of teachers in rural compulsory education schools, as well as the the compulsory education quality in Guizhou, it is necessary to take the following measures: optimizing the authorized size of compulsory school teachers, approving the student-teacher ratio, continuing to implement the Special Contracted Teachers’ Policy, reforming the evaluating policy of the professional titles and recruiting caretakers and so on.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.359
Teacher spread0.278 · 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 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
Published2015
Admission routes1
Has abstractyes

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