Essential Policy Intelligence | Conseils indispensables sur les politiques ECONOMIC GROWTH AND INNOVATION Rethinking Ontario’s Electricity System with Consumers in Mind
Bibliographic record
Abstract
Ontario electricity consumers stand to benefit from lower electricity prices and less risk if the province moves to a capacity market for obtaining generation. Relative to Ontario Power Authority’s 20-year power purchase agreements, a capacity market could better match demand for new power capacity with how much and what type of supply materializes, resulting in less excess capacity and lower prices for consumers. Such a market would also provide a more economical mix of fuels and supply than centralized procurement. Plus, market mechanisms can be supplemented by any government-established environmental regulations and emission controls. Finally, a capacity market would help allocate project development risk to the most logical risk bearer: the project developer. Ontario’s power system is entering another period of transition. After a decade of relying on the Ontario Power Authority (OPA) to procure capacity, rather than relying on market-oriented options, rising power costs have shown the limitations of this approach (Goulding 2013a). The Independent Electricity System Operator (IESO), the market operator, has begun exploring a capacity market for Ontario. In a capacity market, in its purest form, generators receive payments for being available to produce energy if needed at some point in the future, in addition to payments for actual production at a given price, in a given hour (Oren 2000).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".