Industrial Strategies for Green Jobs: Opportunities and Obstacles in the Ontario Case
Bibliographic record
Abstract
Doing something about the environmental crisis without harming the economy and jobs has been a dilemma for governments for many years. This paper explores the potential and opportunities conferred by green jobs economic strategies using the example of Ontario's Green Energy policy. This case also highlights the obstacles to achieving that positive sum result posed by international economic agreements. Trade agreements like NAFTA and the WTO, however, may have an impact on state capacity to enact and implement industrial policies, since green economic strategies can be seen as a particular variant of an industrial strategy. The domestic content provisions in Ontario's Green Energy Act, and alleged subsidization through the FIT have already triggered trade complaints and an action by Japan. Government procurement is a central plank in the defence of Ontario's policy, though one that is threatened by possibly enhanced procurement openness that Canada is negotiating, with provinces at the table, in new economic agreements such as CETA. Outcomes are uncertain but as this case study shows trade and investment agreements do pose a challenge to green industrial policies especially if government procurement protections are sacrificed or substantially weakened.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".