The Costs of Energy-related Linear Property on Local Governments in Canada and the Role of That Local Government Revenue Tool Can Play in Addressing these Costs
Bibliographic record
Abstract
This chapter focuses solely on the various costs that ERLP brings to local governments, the evidence regarding these costs, and the tools that a local government can employ to recover these costs. Given that local governments across Canada are generally constrained to raising revenues through property taxes and various user levies, including those associated with regulation, this paper will focus on the application of local property taxes on ERLP as well as discuss the likelihood of charges that can be levied pursuant to local government regulatory powers, via rights-of-way by-laws, and fees pursuant to local government powers to enter bilateral rights agreements. Overall, the chapter finds that there are options available to local governments to not only take actions to minimize the costs imposed by ERLP but also to recoup the identified costs. Each option not only to minimize costs but also to recoup costs has areas of strengths and weaknesses, suggesting that no only a multipronged approach will be necessary, conditional on the specific jurisdictional characteristics, but also that these tools may not be able to minimize or recoup all the specific costs incurred.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".