Tax Literacy: A Canadian Perspective
Bibliographic record
Abstract
Tax systems are complex structures that can be difficult for individuals to navigate. Understanding the way taxes are calculated and liabilities are assessed matters a lot when personal saving for retirement and education, and much of the government's social policy apparatus, are closely integrated with the tax system. This study uses a survey to measure individuals' knowledge about basic elements of the personal income tax, their perception of their own tax knowledge, and their tax-filing behaviour. One would hope that tax-literate Canadians would have a high level of knowledge of the way taxes work, and a realistic appreciation of the limits of their knowledge, and thus that they could make informed decisions, for example, when filing their tax returns. The survey data show that Canadians have good knowledge of basic tax facts but struggle when asked more complex questions regarding the progressivity of the income tax. Results were generally consistent across provinces with the notable exception of respondents in Quebec, who had higher marks on the authors' tax quiz but lower self-assessed tax knowledge. The measurement instrument employed in the study will allow for a refinement of research exploring the drivers of tax compliance as well as political attitudes toward taxes and redistribution.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".