Corporate Tax Planning: ESG and Corporate Tax Planning
Bibliographic record
Abstract
Environmental, social, and governance (ESG) is a framework for considering certain risks and opportunities applicable to a company. Investors, and increasingly regulators, are requiring disclosure of certain ESG-related metrics and data. Some of these data reflect the externalities that a company creates with respect to the environment and to society. Investors, and potentially regulators and governments, can use these data to evaluate the company and price negative externalities. Being prepared and proactive will allow a company to develop a tax strategy that is consistent with its larger ESG goals. Globally, there is an increasing obligation for companies to publish a tax strategy, as well as to disclose uncertain tax positions and aggressive tax planning to the tax authorities and the public. This trend is also making its way into Canada, albeit at a slower pace. As the Canadian government and investors move toward pricing negative externalities using ESG metrics, it is crucial that Canadian companies start to consider the role of ESG with regard to tax planning, if they have not done so already. Canadian companies should also consider how their tax strategy will be perceived by the company's stakeholders, including the public, clients, and employees.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".