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Record W2689958987 · doi:10.5539/ass.v13n7p150

Multi-factor Consideration in Selection of a Capital for a Country

2017· article· en· W2689958987 on OpenAlexvenueno aff
Dachang Liu

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCapital (architecture)Political capitalPopulationEconomicsSelection (genetic algorithm)Economic systemConsolidation (business)Function (biology)PoliticsVariety (cybernetics)Development economicsPolitical scienceFinanceGeographySociologyComputer science

Abstract

fetched live from OpenAlex

Selection of a capital is of critical importance to security and stability of a country and consolidation of its regime. In selection of the capital in all Chinese dynasties over the past, what the governors usually take into account contained a variety of factors, such as, the strategic military position of a city, its economic development, its traffic convenience condition, its ethnic relations, etc. The same is true with other countries. Consideration of multi-factors is an inevitable route in selection of a capital. With development of the time and swift increase of population, capitals of some countries might be encountered with contradictions between population resources and environmental pressure and political functions. To resolve these contradictions, these countries, one after another, take different measures by moving their capitals elsewhere and decomposing functions. To the end of give play to the political function of a capital and maintain the integration capacity of a country, it is a must to pay attention to and deal with the issue of capital.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.292
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations3
Published2017
Admission routes1
Has abstractyes

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