Automation and Workers: Re-Imagining the Income Tax for the Digital Age
Bibliographic record
Abstract
In the age of automation, more and more workers lose jobs or become gig workers, and the share of labour income in national income is expected to decline further. These developments threaten the sustainability of Canada's 102-year-old income tax as a major source of government revenue and a key instrument for redistributing social income. The authors make the case for re-imagining the income tax to suit the digital age. They propose that all workers should be taxed the same, regardless of the private-law arrangements or technical means used to carry out the work. They call for a reconceptualization of the source of income as human capital, capital, or business. They suggest ways of amending the Income Tax Act to ensure that income from work is not embedded in capital or disguised as active business income that warrants tax subsidies. To ensure the implementation of such re-imagined tax, the authors suggest broadening the scope of withholding tax by taking advantage of technological advances.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".