Beyond ‘Once BITten, Twice Shy’: defending the legitimacy of investor-state dispute settlement in Peru and Australia
Bibliographic record
Abstract
Investment protection is a contentious issue in trade and investment negotiations due in large part to controversy surrounding investor-state dispute settlement (ISDS). Governments have taken a range of positions on ISDS—from opposing moderate reforms to the system to the outright rejection of it. Extant research suggests that countries which experience costly investor claims are more likely to be circumspect about it. Case studies of Australia and Peru demonstrate that other factors must be considered. Both countries experienced investor claims but governments continued to act as ‘pragmatic proponents’ of the system. We show how interest groups and experts shape government preferences by reinforcing the legitimacy of ISDS in the face of contestation. In both cases, domestic actors framed ISDS as low risk; promoting good governance through regulatory chill; and protecting public interests through the promotion of business, which outweighed the costs of participation. Despite the lack of empirical evidence supporting these claims, they were persuasive because interest groups played on embedded ideas about the merits of market-led development and the economic utility of the mechanism. However, we predict that the influence of pro-ISDS actors will vary over time depending on their access to bureaucratic and political decision-making centres.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".