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Record W2978246367

Analysis of Changes in Employment Structure of China Tertiary Industry

2009· article· en· W2978246367 on OpenAlexvenueno aff
LI Jua

Bibliographic record

VenueInternational Business Research · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsnot available
Fundersnot available
KeywordsLaggingTertiary sector of the economyPrimary sector of the economyChinaSecondary sector of the economyUnemploymentGovernment (linguistics)Work (physics)Economic sectorEconomic reformBusinessIndustry of ChinaEconomicsUnemployment rateEconomic growthEconomyPolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Although China's economy is booming more rapidly and widely than ever before since the Chinese economic reform has been implemented,the development of the tertiary industry is lagging behind that of both of primary and second-ary industry and the growth rate of employment of China tertiary industry is lower than that of its production value.However,the tertiary industry still has plenty more potential capacity to create new work opportunities.Therefore,This essay will apply comparative analysis method to research on employment problem in tertiary industry,such as comparing the percentage of employment with that of developed countries,comparing the employment situation with different regions and comparing with various sectors within the tertiary industry to find that traditional services-base sector absorbs too much more labours,which could not solve the unemployment problem essentially because the ability of that sector is very limited to accelerate economic growth.Furthermore,the essay will suggest that the government could large extent develop those modern sectors of the tertiary industry with larger capacity for new work opportunities to boost employment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.427
Teacher spread0.367 · 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 teacher head, not a consensus.

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

Citations1
Published2009
Admission routes1
Has abstractyes

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