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Record W2926396199 · doi:10.5539/jel.v8n3p13

Why Has Japanese Educational Reform Come to a De Facto End?

2019· article· en· W2926396199 on OpenAlexvenueno aff
Mamiko Takeuchi

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Reforms and Inequalities
Canadian institutionsnot available
Fundersnot available
KeywordsDe factoCurriculumGovernment (linguistics)Period (music)Education reformPolitical scienceHigher educationEconomic growthDemographic economicsEconomics

Abstract

fetched live from OpenAlex

A reform of public education in Japan took place from the 1980s, coming to a de facto end in 2013. The reform, which affected large numbers of Japanese children, focused on creating a more flexible, relaxed form of education by reducing the amount included in the curriculum. However, the effects of this reform have been ambiguous, and we therefore aimed to assess them more accurately. We assessed the effects of the reform by looking at the private educational costs of households during the reform period, using the data from a time series survey conducted by the Japanese government. Our evidence shows that the auxiliary study expenses of children in public junior high schools increased steadily, and the proportion of children from households in the highest income group attending private junior high schools also rose during the reform period. This evidence indicates that the reform had unexpected results. It may have triggered a widening of children’s academic ability gap depending on household wealth. There is also no comprehensive evaluation of how pressure-free education affected the academic results of Japanese children. We drew some lessons from this experience to suggest what is needed for successful educational reform.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.353
Teacher spread0.324 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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