Bibliographic record
Abstract
Climate change and the depletion of fossil fuel are no longer a growing concern, but the most time‐sensitive issues facing the society. In response to this, Ontario passed the Green Energy Act (GEA) intolaw in 2009 and introduced a number of initiatives to promote the “green economy” in the province,making it the first North American jurisdiction with an incentive system modelled after Germany’s feed‐in tariffs (FITs). Many believe that the GEA will improve the business conditions for clean technologyendeavours in Ontario; nonetheless, others doubt that people will be susceptible to the higher energyprice and claim that now is not the right time. This paper aims to critically assess the viability of themarket development for renewable energy as proposed by the GEA. Considering that it is relatively earlyto make any conclusion, the first part of this paper provides a brief summary of what the GEA entails andcompares it with the case of another jurisdiction after which the GEA was modelled – namely, Germany. In the second part, this paper closely examines the effects that the GEA has had on businesses in Ontario. More specifically, analysis of ongoing “green energy” projects is provided based on interviews withindustry professionals in both the public and the private sectors in the province.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".