Долговременная конкурентоспособность компаний: основные вызовы
Bibliographic record
Abstract
The loss of competitiveness by global companies that held leading positions in their industries and had significant core competencies for a very long time highlights a need for the analysis of such a phenomenon as long-term competitiveness. The aim of the research is to discover major causes of the loss of competitiveness. The goals include the detection of breaking points that force companies to remold their business activities aswell as the determination of factors that bring about the emergence of breaking points. The subject of sustainable competitiveness relates to both economics and management. That is why numerous theories including the neoclassical one, the institutional one etc. are applied to the study. The research methods include the case-study method as well as structural and comparative analysis. The study of 33 companies (from the USA, Canada, Germany, Sweden, China, South Korea and Japan) in five industries helps to reveal the most common breaking points as well as to determine and classify the internal and external factors that lead to them. The internal factors include crises of growth, diversification crises, innovation crises, reputation crises etc. The external ones include political, technological, economic and natural problems. The authors of the paper reach the conclusion that the loss of competitiveness by a company happens as a result of combined influence of internal and external factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".