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Record W4317460373 · doi:10.55365/1923.x2022.20.69

Innovative Approaches in the System of Regional Development Strategizing

2022· article· en· W4317460373 on OpenAlexvenueno aff
Iryna Ignatiеva, Alina Serbenivska, Анна Орел, Марія Бєлобородова, Liudmyla Bondarenko

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingRelevance (law)Process managementPosition (finance)Strategic planningManagement scienceStrategic managementKnowledge managementComputer scienceBusinessManagementMarketingEngineeringPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Nowadays, the development of effective strategic decisions for the development of the business environment in different areas requires new approaches and tools of strategic management.Among such innovative tools, the authors chose the position of the theory of spiral dynamics developed by Don Beck and Chris Cowan, which was based on the theory of emergent cyclic levels of existence of Clare W. Graves.Of particular relevance is the search for tools in the activities of certain areas, where the active community adopts experience and gains practice, and sometimes financial assistance from international institutions to implement their projects.It is proposed to use benchmarking as a specific methodological tool.The main aim of the study is to highlight the methodological principles of benchmarking in the analysis and formation of strategic directions of regions and search for a methodological tool that would identify not only the model region as an example of best practice in a particular area of strategic development, but also to understand which areas need some improvement to move to a more effective stage of spiral development.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0020.017
Scholarly communication0.0130.006
Open science0.0030.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.122
GPT teacher head0.215
Teacher spread0.093 · 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 designNot applicable
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

Citations2
Published2022
Admission routes1
Has abstractyes

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