Bibliographic record
Abstract
There is a significant debate underway regarding the risks and rewards of foreign direct investment (FDI) for countries in the Global South. These discussions are particularly relevant to the people of Latin America, where the use of inward FDI as a mechanism to support economic development has had dramatic results, both positive and negative. One of the key works in the study of FDI is Robert I. Rotberg’s argument that FDI is critical to support the development of weak states; however, the applicability of this theory faces difficulty in the context of Latin America, where middle-income countries have extractive institutions (Rotberg, 2002). I use the cases of Mexico and Peru to demonstrate that for middle-income countries, extractive institutions can hamper the rewards of FDI and even exacerbate development problems or create new ones. In this regard, the sector of FDI will determine the nature of the impact. In states with extractive institutions, FDI in the natural resource sector is prone to stimulating social conflict. In states with extractive institutions, FDI in the manufacturing sector begets a situation of stagnated development, as the jobs that are introduced are of poor quality and low wages.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".