Ordinary People, Necessary Choices: A Comparative Study of Childcare Expenses
Bibliographic record
Abstract
This Article uses comparative analysis of childcare deductions in the U.S., Canada and Israel in order to expose the non-technical nature of the distinction between business and personal deductions. The comparative analysis reveals significant differences in the ways courts in the three jurisdictions have interpreted the distinction in the context of childcare expenses. Courts in these countries have dealt with the question in different eras and faced different legislative environments. The differences in time and legislative background not only help explain some of the differences among the courts’ decisions in the three jurisdictions, but also expose the normative foundations of the seemingly technical distinction, demonstrating that the issue of childcare deductions in fact raises a question of baseline-setting. In allowing or disallowing the deduction of childcare, courts actually determine what counts — and more importantly what should count — for income tax purposes in general and deductions in particular. By exposing the baseline issues underlying questions of deduction, the Article demonstrates why it is preferable to explicitly use normative considerations in lieu of the so-called technical distinction between business and personal deductions. The comparative analysis further raises an institutional consideration; as shown, precisely because this is a baseline issue, legislatures, who enjoy a larger doctrinal tool box than courts, can offer greater flexibility as required when a variety of normative considerations should be taken into account.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".