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Record W4401811450 · doi:10.55016/ojs/sppp.v12i1.69854

Tax Policy Trends: The Merits of Automatic Income Tax Assessments for Low-Income Canadians

2020· article· en· W4401811450 on OpenAlexaffabout
Lindsay M. Tedds

Bibliographic record

VenueThe School of Public Policy Publications · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomicsIncome taxState income taxGross incomePublic economicsAdjusted gross incomeInternational taxationDividend taxTax policyDemographic economicsLabour economicsTax reform

Abstract

fetched live from OpenAlex

In Canada, the personal income tax system is not just used to raise revenue; it has also become an important instrument for delivering various income support benefits, like the Canada Child Benefit (CCB) and the Climate Action Incentive. By law, a person is not required to file a tax return if they do not owe any taxes. However, by not filing many individuals and families are potentially losing out on thousands of dollars in benefits intended to achieve various economic and social objectives.

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 imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.136
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.010
Science and technology studies0.0080.003
Scholarly communication0.0120.007
Open science0.0040.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.043
GPT teacher head0.358
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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