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Record W2990017675 · doi:10.5509/2019924701

Assumptions and Distortions: Dore on Equality in Japanese Schooling

2019· article· en· W2990017675 on OpenAlexvenueno aff
Merry White

Bibliographic record

VenuePacific Affairs · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsMeritocracyAscriptionArgument (complex analysis)SociologyWork (physics)Selection (genetic algorithm)Gender studiesPolitical sciencePedagogyAestheticsLawEpistemologyArtPhilosophyEngineering

Abstract

fetched live from OpenAlex

Ronald Dore's work on education in Japan centred on themes of selection and equality. In his work on Tokugawa education, Dore presaged some of the emphasis he gave in his later work on quality and social and moral content in modern education. The argument of The Diploma Disease concerned the "late development effect" as a tool in understanding the emphasis on qualification and selection that led to Japan's postwar examination hypertrophy, and in understanding the distortions and inequities that ensued. "Late ascription"—tracking and determining one's life chances with a single examination—was one such distortion, narrowing the gate to educational and occupational success, belying the notion that Japan demonstrates a pure "meritocracy."

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.046
Scholarly communication0.0070.008
Open science0.0010.007
Research integrity0.0030.005
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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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