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Record W2789553561 · doi:10.1177/0972150917713840

Inclusive Growth: Economics as if People Mattered

2018· article· en· W2789553561 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Business Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsUniversity of British Columbia
FundersWashington Center for Equitable Growth
KeywordsWeightingIndex (typography)Inclusive growthEquity (law)EconometricsPrincipal component analysisComputer scienceRank (graph theory)SalientEconomicsMathematicsEconomic growthPolitical scienceArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

This article attempts to fill a gap in the existing literature by providing a holistic working definition of inclusive growth. We measure inclusive growth through a newly proposed index, named as the Inclusive Growth Index (IGI), based on 24 developmental indicator variables (categorized into expansion, sustainability, equity in access, and efficiency of economic activities and institutions) as its components. We have employed two kinds of weighting schemes in constructing the index: an ad hoc weighting scheme and a weighting scheme based on principal component analysis (PCA), performed differently on variables under each dimensions. This index helps one to rank countries or regions according to their respective inclusive growth achievements and to potentially track the time trend of a particular country. In our study, we have calculated IGI for 16 Asian countries and compared the IGI scores across the nations.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.024
GPT teacher head0.335
Teacher spread0.310 · 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