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An Empirical Study on the Performance of the American Government's Responsibility for Compulsory Education

2020· article· en· W3146984280 on OpenAlexaff
Shuwei Harold Sun, Lingsong Nero Wang, Zhaofei Grace Zhou, Shuo Yang, Qi Shao

Bibliographic record

Venue2020 International Conference on Modern Education and Information Management (ICMEIM) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCompulsory educationStatus quoRealmGovernment (linguistics)DutyChinaSocial responsibilityInvestment (military)Power (physics)Economic growthPolitical scienceBusinessPublic relationsEconomicsLaw

Abstract

fetched live from OpenAlex

Over past years, the development of compulsory education in China has been featured with a regional imbalance, especially between urban and rural areas. The root causes of such an imbalance are multifold, mainly due to the unclear division of governmental responsibilities at all levels, the weak commitment of government responsibilities and mutual shirking. In comparison, owing to the advanced experiences of compulsory education, the three levels of government in the U.S. perform respective duty and own individual emphasis, thus forming a relatively stable and clear responsibility structure. Such a responsibility structure greatly promotes the development of compulsory education. Therefore, this paper mainly studies the status quo of both implementation and investment in compulsory education given by U.S. governments, irrespective of levels. Because it is of great inspiration for the Chinese counterparts to enhance its own social public power execution and duty fulfillment, in realm of compulsory education, crossing diverse levels.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.352
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 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
Published2020
Admission routes1
Has abstractyes

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