Hard to Tax Individuals: Indirect Evidence on Their Importance in Canada, 1951-2001
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We begin by discussing what makes it hard to tax some individuals. We then summarize the results of existing US studies on tax evasion, putting the emphasis on the impact of observable and predictable characteristics on the importance of hard to tax individuals (HTTI).We then turn to an examination of the trends in the number and importance (income) of the relevant types of taxpayers in Canada. We do this using a methodology inspired by predictive work done for compliance costs (Blais and Vaillancourt, 1995).These results could be of interest to tax administrators trying to allocate resources across varying regions in a given year or trying to set the proper level of resources to be used for tax compliance work for a given year. Working Paper Number 03-26.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it