Herding Cats: Stakeholder Consultation and 2012 Changes to the National Energy Board Act
Bibliographic record
Abstract
Industrialization in the world market, particularly in Asia, coupled with an increase in Canadian energy production means that Canada needs the transportation infrastructure to bring its energy supplies to market. The most economical way to transport Canadian oil and gas is through pipelines, however the process of regulatory approvals for their construction has become a challenge.1 This is a result primarily of pressure from outside groups like environmental groups, aboriginal groups, unions, and landowners. Designed to evaluate projects on their own merit and potential affect on interested parties, wider societal questions of the oil industry and climate change has crept into the process, making it longer and adding costs to firms and the Canadian economy. By looking at the history of the National Energy Board (NEB), the justification for its creation, and its past performance, this paper will use data from the past ten years as well as relevant theory to address recent (2012) changes to the process used by the NEB. In particular, I will assess whether the legislation will accomplish its goal of a less delay-prone, more responsive approval process that will increase public confidence in pipeline reviews. The intended changes will result in a less delay-prone approval process that still enables public participation. This should have the effect of increasing public confidence in the process.
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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 | 0.040 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| 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".