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Record W3120291882 · doi:10.5430/rwe.v12n2p37

Do the Key Sectors Depend on Changing the Income Level of Countries?

2021· article· en· W3120291882 on OpenAlexvenueno aff
Burcu Oralhan, Eyüp Emre Uluğ

Bibliographic record

VenueResearch in World Economy · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Economic sectorDeveloping countryKey (lock)BusinessDemographic economicsEconomicsDevelopment economicsEconomic growthGeographyEconomy

Abstract

fetched live from OpenAlex

This study aims to identify key sectors in countries showing any class change in level of income and to examine whether there is any relationship between sectors and income classes. Another aim of the study is to identify emerging and disappearing sectors in key sectors during periods when countries’ income levels change. In this context, four basic income classes published by the World Bank are examined for 43 countries but class change was identified only in 12 countries between 2000-2014. A statistical difference was determined between the sectors in the classes at Low and Low Middle levels and Upper Middle and High) classes. Among these countries discussed in the 15-year period with 56 sectors examined, some sectors showed feature of being common key sectors, some tendency to be a key sector in recent years and some sectors have lost their key feature before or after the direct income class changes.

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)0.004
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.111
GPT teacher head0.356
Teacher spread0.245 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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