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Record W3177359574 · doi:10.6000/1929-7092.2019.08.74

Management of Reputation Risks at the Agricultural Enterprises of Eastern Europe as a Component of Increasing Their Competitiveness

2021· article· en· W3177359574 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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of Reviews on Global Economics · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Business Development Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsReputationAgricultureComponent (thermodynamics)BusinessIndustrial organizationAgricultural economicsNatural resource economicsEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

We note a significant role of the agricultural sector in the development of economic systems in a significant number of post-Soviet countries of Eastern Europe. However, Eastern European agricultural enterprises have significant problems in ensuring and managing their competitiveness, where reputation and the risks associated with it are of key importance. Novelty. The scientific novelty of the research paper is the developed algorithm of reputation risk management, which is based on the author's methodology of their evaluation and takes into account the peculiarities of such management in agricultural enterprises from the post-Soviet countries of Eastern Europe. To achieve the goal and test the hypotheses put forward in the research paper, a set of general, specific and technical methods were used at the empirical and theoretical levels, such as: abstraction method; expert method; methods of analysis and synthesis; comparison; deduction; induction; methods of systematization, grouping and logical generalization. The research methodology is based on systemic and functional, historical and systemic approaches in identifying and resolving the range of problems of reputation risk management within the framework of improving the competitiveness management of agro-industrial enterprises from the post-Soviet countries of Eastern Europe. For the purpose of the study, data were collected and an empirical analysis was conducted concerning the eleven Eastern European countries that were part of the Soviet Union for 1991-2018 regarding analysis of the dynamics of agricultural production and its share in GDP according to statistics taken from the KNOEMA databases. Policy considerations: the agricultural sector of the economy plays an increasing role in the economic systems of some post-Soviet countries of Eastern Europe, serving as the basis for their sustainable development; agricultural producers from the post-Soviet space of Eastern Europe have problems with ensuring competitiveness in national, international and world markets; reputation risk plays a significant role in ensuring and improving the competitiveness management of agricultural enterprises from post-Soviet countries of Eastern Europe; the formation of an effective reputation risk management algorithm is a key element in ensuring and improving the competitiveness management of Eastern European agricultural producers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.266
Teacher spread0.202 · 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