Bibliographic record
Abstract
The issue of which in-kind benefits should be taxed and how these benefits should be valued have concerned tax legislators, administrators, and academics since the introduction of the personal income tax system. Building her theoretical analysis on the income concept advanced by Henry Simons and relying on traditional tax policy notions of equity,neutrality, and administrative practicality, the author asserts that employees must be fully taxed on employer-provided in-kind benefits. To this effect, the article offers guidelines for distinguishing between taxable in-kind benefits and non-taxable conditions of employment. The author argues that the correct method of valuation of in-kind benefits is their fair market value, rather than the cost to the employer or the subjective value of the benefit to the employee. The article proceeds by revealing inconsistencies, inequities, and inefficiencies that have resulted from the manner in which Canadian tax administrators and courts have delineated taxability of in-kind employee benefits. Discussing such in-kind benefits as educational courses, employee trips, clothing, subsidized parking, and discounts on goods and services, the author makes suggestions that may be utilized to develop a matrix of detailed and consistent rules on the taxability of these and others in-kind benefits.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".