MétaCan
Menu
Back to cohort
Record W3137818815 · doi:10.33917/es-4.170.2020.72-79

Uncertainties in Pandemic Assessment. Chronicles of Appraisal Business

2020· article· en· W3137818815 on OpenAlexaboutno aff
Sergey A. Pobyvaev

Bibliographic record

VenueEconomic Strategies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)BusinessPerformance appraisalQuarter (Canadian coin)Position (finance)Process managementStrategic managementMarketingQualitative analysisQualitative propertyQuantitative analysis (chemistry)Industrial organizationQualitative researchManagementComputer scienceEconomicsFinance

Abstract

fetched live from OpenAlex

Methodology for studying the strategic potential of Russian appraisal companies is based on applying the “Strategic Matrix of the Company” software package, the algorithm of which involves analysis of qualitative and quantitative indicators that allow to create a complete company profile and to provide a scenario forecast for its development [1]. The main qualitative parameters accepted for analysis include such as the degree of innovativeness and differentiation of services, the level of competition and the company’s position in the market, priority of various strategic goals in the company’s activities, motivation effectiveness, corporate culture and the ability to quickly gain access to necessary resources. The present study resulted in the rating of 50 the most strategic appraisal companies in Russia according to results of the second half of 2019 — the first quarter of 2020.

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)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.004
Science and technology studies0.0020.004
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.352
Teacher spread0.302 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEconomic StrategiesSame topicEconomic and Technological Developments in RussiaFrench-language works237,207