Responsible Scholarship in a Crisis: A Plea For Fairness in Academic Discourse on Carbon Pricing References
Bibliographic record
Abstract
The Canadian federal government’s carbon pricing legislation has generated substantial public and academic debate. In this paper we argue that academic debate should adhere to standards for responsible conduct of research during crises such as the current climate emergency, and avoid the nastiness and distortion that infect populist political rhetoric and social media. We discuss the norms of responsible scholarship that apply to Canadian legal academics, with a focus on standards that demand scrupulous fairness to other scholars and to the materials one is analyzing. We argue that a recent article by Professor Dwight Newman on the Saskatchewan and Ontario reference cases upholding the constitutionality of the federal carbon pricing law does not live up to these standards in two ways. First, it treats other scholars unfairly by distorting their scholarly work and lumping them into derogatory, unsubstantiated general types. Second, it is unfair to the legal materials under consideration by portraying the relevant case law in an unduly selective manner to advance the author’s argument. We close the paper with some reflections on why this particular case matters.
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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 | 0.057 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.042 | 0.104 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".