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Record W7038015547

Fiscal Contract and the Canada Disability Benefit: Lessons from Income Tax Law

2023· article· en· W7038015547 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2023
Typearticle
Languageen
FieldMedicine
TopicAndrographolide Research and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIncome taxState income taxEquity (law)Gross incomeInternational taxationTax reformIndirect taxDouble taxation
DOInot available

Abstract

fetched live from OpenAlex

This paper adopts a fiscal contract approach to examining the design of the Canada Disability Benefit and advocates using the CCB as a design model. It argues that a fiscal contract underlies the Income Tax Act which collects taxes as well as spends public money on poverty-reduction programs. In a tax state, the government’s spending is tied to taxing. The current fiscal contract reflects key Canadian values, such as equity and fairness, ability to pay, work, family and the rule of law. The CCB, which is implemented through the Income Tax Act, is a superior design model to the Guaranteed Income Supplement for designing the new benefit.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.144
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0070.014
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.019
GPT teacher head0.296
Teacher spread0.278 · 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
Published2023
Admission routes1
Has abstractyes

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