Identifying and Ranking Factors Influencing on Investor Attraction in Golestan Province by Means of Fuzzy Multi-Index Decision-Making (FAHP)
Bibliographic record
Abstract
There is no doubt that attracting investor in economic and industrial sectors is one of the key and effective issues. We can take giant strides toward improvement with the advent of investors in infrastructure sectors. The research is the result of an applied research with the aim of identifying and ranking factors which are effective on attracting investor under fuzzy environment. Hence analysis hierarchy process (Ahp_Fuzzy model) was suggested. The research method is descriptive-survey, where factors which influence on investor attraction in Golestan province, had been identified in terms of research literature. The factors were prioritized based on comments of 25 senior managers and economic experts in management and planning organization and chamber of commerce through Ahp. The research findings indicate that lack of a coordinated and efficient plan for identifying weakness, opportunities, and intimidations is the most important problem and it is a fundamental obstacle in Golestan province in order to attract investors and to specify an appropriate strategy with significance coefficient of 0.227. There are some other obstacles for attracting domestic and foreign investors in the province which are as follows:Lack of appropriate administrative organization, lack of right and efficient management with significance coefficient of 0.220, lack of primary infrastructure facilities and fundamental infrastructure and public services with significance coefficient of 0.204, limitation and lack of flexibility in rules and regulations related in investing in Goelstan province with significance coefficient of 0.198 and finally side effect of policy makings of government in macro level with significance coefficient of 0.151.In fact, the research results are exactly compatible with current economic condition of the area. We can establish a comprehensive outlook for government and senior managers of the area according to level of significance and level of influence of the indices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".