MétaCan
Menu
Back to cohort
Record W2073769259 · doi:10.5539/res.v6n4p82

Diagnostics of Effective Risk Management Strategies on the Basis of Synergetic Effect Evaluation

2014· article· en· W2073769259 on OpenAlexvenueno aff
Taymaskhanov Hassan Elimsultanovich, Tsakaev Alkhozur Kharonovitch, Musaev Lemi Akhmedovich

Bibliographic record

VenueReview of European Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEconomic and Technological Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsConsistency (knowledge bases)Basis (linear algebra)Yield (engineering)Risk analysis (engineering)Risk managementManagement scienceBusinessComputer scienceEconomicsMathematicsArtificial intelligenceFinancePhysicsThermodynamics

Abstract

fetched live from OpenAlex

In this article we develop a hypothesis that there are risk management strategies, which yield to monitoring on the basis of synergetic effect evaluation. The technique of synergic effect evaluation suggested by authors is described. On the basis of financial accounting of JSOC “Bashneft” and JSC “Sberbank Rossii” the consistency of hypothesis of synergetic effect indicator applying for monitoring of such risk management strategies as M&A deals and economic activities spheres ?lustering is confirmed.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.633
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
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 head0.022
GPT teacher head0.263
Teacher spread0.242 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueReview of European StudiesSame topicEconomic and Technological Systems AnalysisFrench-language works237,207