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Record W7132827917 · doi:10.35774/econa2025.02.144

International experience of institutional support for small business in conditions of economic imbalances

2025· article· W7132827917 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconomic Analysis · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipSmall businessCorporate governanceOrder (exchange)Adaptation (eye)Service (business)Psychological resilienceInstitutional analysis

Abstract

fetched live from OpenAlex

This article examines the international experience of institutional support for small businesses under conditions of economic imbalances, with a particular focus on practices in the European Union, the United States, Canada, and South Korea. The study highlights the role of institutional mechanisms in ensuring the resilience of SMEs during crisis shocks such as the COVID-19 pandemic, energy instability, and structural transformations. It analyzes financial, regulatory, and partnership tools for supporting small businesses across centralized, decentralized, and network-based governance models. Special attention is given to the development of a prospective institutional policy model for SME support in Ukraine, taking into account the country’s post-war recovery needs and limited fiscal space. The purpose of the article is to conduct a systematic analysis of the international experience in institutional support for small enterprises under economic imbalances—focused on practices in the EU, USA, Canada, and South Korea—in order to identify effective policy models and financial-regulatory tools for SME support, and to develop proposals for their adaptation to Ukraine’s socio-economic conditions amidst ongoing war-related and post-crisis challenges. Methodology. The research applies methods of comparative analysis, structural-institutional approach, content analysis of official SME support programs, and elements of strategic planning to elaborate an adaptive model of institutional policy. Results. The study finds that the most effective models of SME support combine financial inclusiveness, digital service delivery, regulatory sensitivity, and public-private synergy. For Ukraine, the article proposes institutional transformations such as establishing an SBA-type agency, implementing the SME-test, expanding regional SME support centers, and launching public-private startup accelerators. This model is deemed capable of fostering SME resilience, innovation, and competitiveness under martial law and during the post-crisis recovery period.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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