Risk and Return: Foreign Direct Investment and the Rule of Law
Bibliographic record
Abstract
This is the report of a survey conducted by the Economist Intelligence Unit, on behalf of Hogan Lovells, the Bingham Centre for the Rule of Law, and the Investment Treaty Forum of the the British Institute of International Comparative Law, on the relationship between corporate Foreign Direct Investment (FDI) decision-making and the Rule of Law. The survey seeks to identify the factors multinational corporates consider in selecting where to invest internationally, and to gauge in particular the role of the Rule of Law, defined as ‘certain, accessible and prospective laws; equally enforced; with access to justice (…) where rights may be asserted (…) through fair trials before an independent judiciary’. The survey was conducted with 301 senior decision makers at Forbes 2000 companies with global annual revenues of at least USD1bn. Most companies surveyed were headquartered in the US and Canada (40.9%), Western Europe (32.9%) and Asia (14.3%). Respondents represented companies operating in a variety of industry sectors, including financial services (19%), information industries and telecommunications (16%), energy and natural resources (15%) and healthcare, pharmaceuticals and biotechnologies (15%). The analysis and report is solely the work of the Bingham Centre for the Rule of Law and the Investment Treaty Forum.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".