The Logic, Policy and Politics of Tax Law
Bibliographic record
Abstract
Written by a team of prominent law professors, with each passing day, Materials on Canadian Income Tax is getting more and more familiar among the students of Canadian income tax. From expert commentaries to legislation and govt policy references and case analysis, one can turn the pages of this well-edited book for clear, concise information.\nAlong with in-depth case studies, this casebook combines the authors' analysis and the extracts of tax cases and relevant references to govt policies.\nThis book includes references to: Proposed and enacted legislation Recent budget proposals Interpretation Bulletins Canada Revenue Agency procurements \nThe other salient features of this book include: It has a set of finding tools to help you find what you want to read, such as a table of statutory references, a table of cases, and a topical index The book has an organized research source of Canadian income tax principles and practices that can guide both students and practitioners through their research \nHere is what's new in this edition: Reflects the law as of March 31, 2020 Further analysis of Canada's income tax regime Discussion on the latest developments in the case law and Canada Revenue Agency pronouncements Details regarding the updates in the statutory tax law Policy discussions that introduce you to complex tax laws Organized to provide quick access to the relevant tax law concept
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".