The CCGES Transatlantic Energy Conference
Bibliographic record
Abstract
Competitive electricity markets are artificial markets with extensive rules for all participants arising from the complex interconnections of the electricity network. Governments or regulatory agencies oversee the market design process and the operation and maintenance of the market, so market design is necessarily a political process. The conceptual design of the market must recognise the political forces that will operate on the market design process so that the political process will not thwart the intended outcome of the market as it has in some jurisdictions including Ontario. The limited ability of consumers to understand changes in the electricity sector in the short run poses a real constraint on what can be achieved politically. Letting the market set the price means that governments cannot ensure any particular future price level and both theory and experience tell us that prices may increase after restructuring (California, Ontario, Alberta). This makes it difficult to sell restructuring to consumers who will be interested in the price they pay and not much interested in abstractions like efficiency. Another challenge for electricity restructuring is that the starting points differ from one
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.202 | 0.083 |
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".