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Record W3019032419 · doi:10.14505//jemt.11.1(41).15

Environmentally Oriented Anti-Crisis Management of Enterprises: Problems, Directions, and Prospects

2020· article· en· W3019032419 on OpenAlexaboutno aff
Ainur M. Baltabayeva, Bibigul KYLYSHPAYEVA, Zhanna Assanova, Gulshat Zhunussova

Bibliographic record

VenueJournal of Environmental Management and Tourism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsAuditBusinessChinaWork (physics)Environmentally friendlyEnvironmental crisisFinancial crisisGreen economyCrisis managementEconomicsAccountingSustainable developmentPolitical scienceManagement

Abstract

fetched live from OpenAlex

The article deals with the issues of environmentally oriented anti-crisis management of enterprises. The authors consider environmentally oriented logistics approaches, analyze the ecological and economic status of the Republic of Kazakhstan, and study the experience of developed countries, such as the United States, Germany, Canada, Ireland, China, and Korea, in managing the green economy. Based on international experience in environmentally oriented economic management, promising tools for managing the transition to a green economy have been developed. According to the authors of the article, the main tools of environmentally oriented anti-crisis management of enterprises are the following: conducting a mandatory environmental audit to determine unbiased findings on the environmental impact; creating regional environmental funds for financial support of environmental activities of enterprises; and trading quotas for limited environmental impact. The theoretical and practical significance of the work lies in the possibility of using the main provisions and conclusions in the development of a contemporary market concept of anti-crisis management of the green economy of both an individual enterprise and the country in general.

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 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)0.000
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 score0.428
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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