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Record W2125929033 · doi:10.5539/ass.v10n24p96

Financing Problems of Small and Medium Business in Kazakhstan

2014· article· en· W2125929033 on OpenAlexvenueno aff
Abdibekov Saken Ualikhanovich, Kantureev Mansur Tasybayevich, Bleutayeva Kulzhamal Begimbayevna, Bedelbayeva Assel Erikovna, Kasenova Aybarshyn Mamlenovna

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Systems and Logistics Management
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)BusinessSmall businessBusiness developmentFinanceSmall and medium-sized enterprisesThe RepublicBusiness administration

Abstract

fetched live from OpenAlex

In article theoretical provisions and practical recommendations about improvement of forms and methods of financing of the enterprises of small and average business in modern conditions of development of economy of the Republic Kazakhstan were considered. For achievement of the put purpose in work the following tasks were solved: the main forms and methods of financing of subjects of small and average business were revealed, the main problems of forms and methods the financings constraining development of financing of small and average business in PK were defined, and also the main directions on improvement of forms and methods of financing of the enterprises of small and average business in PK are developed. The practical importance of article consists in possibility of use of the conclusions formulated in work, offers and recommendations when developing nation-wide actions for support and stimulation of development of small and average business of the state, an also financially-credit institutes in practice.

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)0.007
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 score0.015
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.210
Teacher spread0.180 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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