Bibliographic record
Abstract
As the federal government weighs policy options for reducing greenhouse gases, the question arises as to how to treat imported goods from countries with less stringent emission targets. One policy option is to impose a “carbon tariff ” on imported goods from those countries. The imposition of such a border adjustment, however, would have serious drawbacks. The largest share of the burden of any Canadian trade action would be imposed on US imports, not developing countries. An extra cost imposed on imports would ripple through the economy, and onto final consumers, and would put Canadian firms at a cost disadvantage in foreign markets. The author would like to thank Roland Ismer for his helpful discussions of early versions of this work and Finn Poschmann, Alex Laurin, Aaron Cosbey, Richard Morgenstern, and especially Colin Busby for helpful comments. Thanks also to Nic Rivers for providing research results that were used in the analysis. The author assumes all responsibility for this paper. 1 See Hufbauer (1996) or Orzechowski (2001) for a comprehensive review of border-tax adjustments (BTAs). The failure of the United Nations Climate Change Conference in Copenhagen to reach a binding global agreement to reduce greenhouse gases (GHGs) raises the prospect of a world with fragmented national
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.357 | 0.170 |
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".