Identification of the Best Sector for Multinatinal Companies Investment Using the Q-rung Double Hierarchy Linguistic Term Set Todim Approach
Bibliographic record
Abstract
Abstract The Multinational Companies started with the rise of colonialism in the world. As a major drive of colonialism, the early multinational corporations built "factories" in the port cities of colonized countries. Now, Multinational Companies (MNCs) are investing in different sectors of the economy. However, asset allocation decisions still require proper investigation of industries and countries where the MNCs must invest. Moreover, decision-making becomes more difficult due to incomplete data, uncertainty, psychological and cultural issues in different countries, and hesitancy of the decision-makers (DMs). To overcome these issues, sophisticated decision-making techniques must be employed, so we propose the TODIM approach under the environment of q-rung orthopair double hierarchy linguistic term set (q-RODHLTS), which is the hybrid of q-rung orthopair fuzzy set and double hierarchy linguistic term set. Further, we define basic operations like addition, multiplication, scalar multiplication, exponential, and the comparison functions and distance of q-RODHLT sets. Finally, some applications related to the different sectors for investment by a multinational company (MNC), such as Tourism, energy, and education, are presented using the proposed approaches with comparative analysis. We conclude that energy is the best sector for investment by the MNCs. 1991 Mathematics Subject Classification. 03E72, 03B52, 90C70, 47S40, 15B15.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".