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Record W7096683019

Regional Imbalance under Federal Structure: A Comparison of

2014· article· en· W7096683019 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityRevenueCapital (architecture)DemocracyPopulationProperty rightsShift-share analysisPublic good
DOInot available

Abstract

fetched live from OpenAlex

The problem of regional disparity in economic development in geographically large democratic countries gets inseparably linked with macro public policies and the economic philosophies behind them. Two such countries, India and Canada, are considered in this paper. Although the two countries share several common features, they differ considerably in the size of the population, nature of the federation, constitutional provisions defining property rights on minerals and revenue sharing arrangements between the centre and the states, and the economic philosophy behind the macro policies of the governments. This paper addresses the issue of regional imbalance under federal structure in a compara-tive perspective. The following variables are used to analyse the regional problem: • worker rate • capital productivity • capital intensity • industrial structure. The paper argues that free and barrier-less mobility of population and goods across the states in Canada has resulted in the regional problem getting diluted and less intense. In India,

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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
Published2014
Admission routes1
Has abstractyes

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