The Disability Tax Credit: Exploring Attitudes, Perceptions, and Beliefs About Disability
Bibliographic record
Abstract
This article examines the disability tax credit (DTC), one of the few federal programs providing direct funding to persons with disabilities. The goal of the article is to explore the DTC as a window into attitudes, perceptions, and beliefs about disability. This is an important contribution to the literature as disability scholars have shown how societal forces, such as attitudes, perceptions, and beliefs about disability, contribute to disability itself. Through comparing attitudes, perceptions, and beliefs of legislators and judges with the views expressed in the disability literature, the article reveals harmful stereotypes that fail to take into account the diverse realities of those experiencing disability. Further, the article shows a lack of agreement between four groups: disability scholars, legislators, judges, and taxpayers, on issues relating to the meaning of disability and the appropriate policy response to disability. The author contends this disagreement is troublesome as it may impede progressive policymaking and legal clarity. In particular, the evaluation and interpretation of the DTC is hindered by the lack of agreement about whether the DTC is intended to serve as income support for persons with disability (thus, operating as a tax expenditure), or to take into account costs negatively impacting ability to pay (thus, operating as a technical provision.) Further discussion and investigation of the DTC and the broader disability-related questions raised in this article are warranted.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".