Economic Approach to Conflict Issue: Investment in Post- Conflict Situation for International Business
Bibliographic record
Abstract
The latest Global Economic Crisis and the latest Sovereign Crisis in the euro area have substantially deepened. Financial and economic conditions became a challenging matter for many investors and business organizations. The latest economic outlook is also prominent problem of researchers questioning the methods of sustaining long term interethnic peace in post- conflict countries (PCCs) and economies whilst the economic slowdown has effects on prosperity and development. In this study, it is aimed to develop an interdisciplinary approach to conflict issue within a theoretical framework in order to contribute to success of strategic decision making process at corporate level. Strategy makers at this level must evaluate the nature of conflict and develop conceptual skills before attempting to invest in conflict-prone economies. In this study, economic dimensions of conflict and its effect on investment climate have been evaluated to guide international business organizations. This study demonstrates that (i) there is strong tie between economic conditions and conflict risk, (ii) an increase in employment and income level in post conflicted economies is likely to decrease the probability of future conflict risk among interethnic groups (iii) inequality of income and resource distribution priorities among members of different ethnic groups escalate the risk of conflict, subsequently (iv) the success in the process of economic rehabilitation and recovery is a key contributory factor in sustaining peace and prosperity.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".