Bibliographic record
Abstract
Abstract What are the consequences of being sued for violating bilateral investment treaties? The conventional wisdom is that investor–state disputes (ISDS) tarnish countries' compliance records, and harm foreign direct investment in the process. This article re-examines this belief in light of recent trends in ISDS. The regime has witnessed a proliferation of claims, a growing proportion of which allege breaches of provisions like fair and equitable treatment and indirect expropriation. Combined with a decrease in the rate of success of such claims, the authors argue that the average ISDS claim now contains less information than it once did. If this is the case, investors should be less likely to update their expectations and reduce investments. This study examines 812 investor–state disputes from 1987 to the present day, and finds consistent evidence for this across two different datasets relating to firms' risk perceptions. Consequences of investor–state claims on foreign direct investment are only apparent in cases that allege direct expropriation. Even among these, the effects are smaller today than they were in the past. In sum, the reputational effects of ISDS claims appear to have been eroded by the developments of the last two decades. ISDS just isn't what it used to be.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".