Bibliographic record
Abstract
By far the most significant development in Canadian climate change law and policy in 2016 was the federal government’s announcement of its plan to impose a floor price on carbon. If implemented, the plan would establish a minimum carbon price throughout Canada’s provinces and territories. The plan, however, could also be the subject of a constitutional challenge. On 22 April, the federal government signed the Paris Agreement, which was then ratified on 5 October. In anticipation of ratification, the federal government announced a floor price on carbon of CDN $10 a ton by 2018 and CDN $50 a ton by 2022. The federal government explained that this price would be imposed within the provinces and territories that had not adopted their own carbon-pricing scheme. The federal government expressed no preference as to whether provincial and territorial carbon pricing schemes were based on cap and trade (which is in place in the provinces of Quebec and, by 1 January 2017, in Ontario) or a carbon tax (which is in place in the province of British Columbia). The federal government, however, would require that the scheme impose the requisite floor price on carbon by 2018. Few details of the plan have been made available, but the federal government maintains that floor pricing would be ‘revenue neutral’ for the federal government. In other words, revenue generated by the federal government would presumably be remitted to the province or territory such that it would not leave the jurisdiction.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.031 |
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".