Bibliographic record
Abstract
This chapter discusses three findings of a study on whether investor–state dispute settlement (ISDS) has contributed to changes in government decision-making about environmental protection in Ontario, Canada. These findings are: (1) ISDS puts pressure on government decision-making due to the financial and political risks, the opportunity costs that ISDS creates for government, and as a consequence of the career risks that it creates for individual officials; (2) ISDS pressures may be overcome, especially where there is a strong political commitment to a proposed measure backed by legal capacity to scrutinize purported ISDS risks critically and throughout the policymaking process; (3) the assessment of trade or ISDS risks involves value choices and ISDS-generated changes to decision-making processes elevate the role of ‘trade values’ over competing values associated with health and environmental protection.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".