The Effect of Foreign Direct Investment on the Hospitality Industry in Liberia: A Case Study on the Chinese Investment
Bibliographic record
Abstract
Foreign Direct Investment is said to have a positive impact on the development of the hospitality industry in developing countries. It helps create employment opportunities and positively impact local economies. The present study research published articles, desk reviews, scientific databases among others to report the results. Current findings showed that the success of the hospitality industry in developing countries depends on the levels of Foreign Direct Investment. Although many developing countries have natural features such as beaches, rivers, and other natural resources, local capital to invest in those resources is unavailable. Tourism shows particular promise for developing countries. The tourism industry is one of the largest and fastest-growing sectors in the global economy and a key driver of socio-economic development, as it is labor-intensive and stimulates SME growth and investment. It has been used in other countries as an economic driver for growth which can widely support poverty reduction.Foreign Direct Investment is said to have a positive impact on the development of the hospitality industry in developing countries. It helps create employment opportunities and positively impact local economies. The present study research published articles, desk reviews, scientific databases among others to report the results. Current findings showed that the success of the hospitality industry in developing countries depends on the levels of Foreign Direct Investment. Although many developing countries have natural features such as beaches, rivers, and other natural resources, local capital to invest in those resources is unavailable. Tourism shows particular promise for developing countries. The tourism industry is one of the largest and fastest-growing sectors in the global economy and a key driver of socio-economic development, as it is labor-intensive and stimulates SME growth and investment. It has been used in other countries as an economic driver for growth which can widely support poverty reduction.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".