Making FDI More Sustainable: Towards an Indicative List of FDI Sustainability Characteristics
Bibliographic record
Abstract
Reaching the SDGs has become the lodestar of development policymaking. Increased sustainable FDI flows to developing countries can make an important contribution to reaching the Goals. This article analyzes 150 instruments (treaties, standards, codes) prepared by key stakeholder groups in the FDI space and bearing on the relationship between host country governments and foreign investors, to identify FDI sustainability characteristics along the following four dimensions: economic, social and environmental development and governance. These instruments indicate the kind of contributions governments and intergovernmental organizations expect MNEs to make to host countries; what kind of contributions MNEs and business organizations expect to make to host countries; and what others expect from MNEs in this respect. The analysis yields a set of indicative “common FDI sustainability characteristics”, as well as a set of indicative “emerging common FDI sustainability characteristics”, with all stakeholder groups showing a growing propensity to recognize them. These indicative FDI sustainability characteristics, in turn, can give guidance to both legal and policy development regarding the role international investors can, and should, make to reach the SDGs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".