Can developing countries grasp the horn of Amalthea? The impacts of FDI on the development of host countries
Bibliographic record
Abstract
By analyzing 150 articles published between 2010 and 2023, this systematic literature review explores the diverse effects of foreign direct investment (FDI) on developing countries across economic, social, environmental, and sustainability dimensions. The research underscores that the ability of FDI to stimulate growth largely depends on the host country’s absorptive capacity, including factors such as human capital, financial institutions, and infrastructure. It highlights the complexities involved in evaluating FDI’s economic impacts, given the varied findings across studies, and emphasizes the growing significance of environmental and social considerations aligned with sustainable development goals. The key contributions of this study include a comprehensive synthesis of the current literature on FDI impacts, critical analysis of host countries’ absorptive capacities, exploration of discrepancies in economic impact assessments, a focus on environmental and social dimensions within FDI research, and policy recommendations aimed at harnessing FDI for sustainable and inclusive development in developing countries. These insights are crucial for comprehending the multifaceted role of FDI and devising strategies for its optimal utilization in emerging economies.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".