Internalisation of International Investment Agreements in Public Policymaking: Developing a Conceptual Framework of Regulatory Chill
Bibliographic record
Abstract
Abstract The growing number of public policy measures challenged through investor‐state dispute settlement has raised critiques that international investment agreements could lead governments to avoid introducing new policy measures out of a fear that these could be challenged by foreign investors, often referred to as ‘regulatory chill’. While the body of work on regulatory chill is still in its infancy, there is a need to interrogate extant studies to better understand the state of the knowledge and the methodological approaches being employed to produce an evidence base. Grounded in a critical review of the existing literature, this paper develops a conceptual framework of regulatory chill, identifying it as one possible policy response wherein investment agreements are internalised by policy makers as considerations during their policy decision‐making. Three distinct bodies of work were identified in the literature which helped to populate this framework, including analysis of investment treaty language and awards, interviews with policy makers to explore internalisation of such treaties, and case studies of suspected regulatory chill policy responses. The conceptual framework is intended to help drive forward a cohesive research agenda on regulatory chill that can underpin the ongoing investment treaty reform.
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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 | 0.075 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.110 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".