Sustainable Development Goals as a Milestone of Strategic Alliances: A Viewpoint From the Perspective of the Game Theory
Bibliographic record
Abstract
Abstract Purpose The goal of this work is to determine the nature and potential of the impact of the development of strategic alliances on the achievement of the key Sustainable Development Goals (SDGs) of the states. Design/Methodology/Approach The study is involved with the use of the universalisation method, statistical analysis method, trend-based method, simulation-based game approach and correlation analysis method. The values of estimated indicators have been determined through the use of these global rankings, identifying their level between 2018 and 2021 in the countries which rank among the leading countries in the field of development of strategic alliances (Malta, Canada, Sweden and Israel). Findings It has been established that sustainable economic development of strategic alliances scarcely ever has a positive impact on the achievement of the SDGs of the states. It has been established that such interaction is possible if these business entities observe certain terms ensuring the necessary development parameters of components of sustainable development. Conditions of the achievement of effect from the impact of business associations of these goals have been identified empirically. It has been proven that such business associations as strategic alliances due to the range of their activity and the potential associated with it can act as economic institutions that complement the functions of the state towards the achievement of sustainable development milestones. Originality/Value The academic novelty of this research is that it substantiates the potential to secure the impact of strategic alliances on the achievement of certain SDGs associated with the observance of certain organisational and economic conditions of strategic management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".