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Record W4312156059 · doi:10.1017/s1468109922000329

Japan: the harbinger state

2022· article· en· W4312156059 on OpenAlexaff
Phillip Y. Lipscy

Bibliographic record

VenueJapanese Journal of Political Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of CambridgeHarvard University
KeywordsSkepticismScholarshipGeopoliticsPoliticsPolitical scienceState (computer science)Foreign policyPolitical economyDevelopment economicsSociologyLawEconomicsEpistemology

Abstract

fetched live from OpenAlex

Abstract Why study Japan? Research on contemporary Japanese politics and foreign policy faces headwinds from the relative geopolitical decline of Japan and scholars skeptical about single-country studies. An overview of Japanese politics publications in English-language journals over the past four decades suggests the subfield remains active and robust. However, there is still room to grow. I argue that Japan is a harbinger state, which experiences many challenges before others in the international system. As such, studying Japan can inform both scholars and policymakers about the political challenges other countries are likely to confront in the future. In turn, scholarship on Japan offers a critical opportunity to develop theoretical insights, assess early empirical evidence, and offer policy lessons about emerging challenges and the political contestation surrounding them. I consider the reasons why Japan so often emerges as a harbinger across issue areas and suggest areas for ongoing scholarly attention.

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)0.003
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.009
Scholarly communication0.0060.006
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.350
Teacher spread0.320 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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