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Record W2568802779 · doi:10.1093/ssjj/jyw044

Declining Self-Employment in Japan Revisited: A Short Survey

2016· article· en· W2568802779 on OpenAlexaboutno aff
Ryo Kambayashi

Bibliographic record

VenueSocial Science Japan Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-employmentMainstreamQuarter (Canadian coin)EconomicsEntrepreneurshipWelfareLabour economicsRebootSelfWork (physics)Self employedSurvey data collectionDemographic economicsPolitical scienceMarket economyPsychologyGeographySocial psychology

Abstract

fetched live from OpenAlex

Although the decline of self-employment has been a fundamental factor in the environment of the Japanese labor market for more than a quarter of a century, economic research on self-employment has remained sparse. This short survey aims to reboot the empirical research. First it summarizes two mainstream literatures: (a) the relation between the business cycle and being self-employed and (b) the entrepreneurship aspects of self-employment. The survey concludes that neither can fully explain the decline of self-employment in Japan. We then point to a relatively new literature on (c) the nonpecuniary rewards of self-employment. While this literature is still developing, it suggests that declining self-employment can be related to a decline in welfare. In particular, comparisons of the welfare implications of self-employment versus non-standard work will be important for deepening our understanding of contemporary Japanese society.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.357
Teacher spread0.298 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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