Bibliographic record
Abstract
Why tax law is not just a pocketbook issue but a reflection of what and whom we, as a society, value. Most of us think of tax as a pocketbook issue: how much we owe, how much we'll get back, how much we can deduct. In Our Selfish Tax Laws, Anthony Infanti takes a broader view, considering not just how taxes affect us individually but how the tax system reflects our culture and society. He finds that American tax laws validate and benefit those who already possess power and privilege while starkly reflecting the lines of difference and discrimination in American society based on race, ethnicity, socioeconomic class, gender, sexual orientation and gender identity, immigration status, and disability. Infanti argues that instead of focusing our tax reform discussions on which loopholes to close or which deductions to allow, we should consider how to make our tax system reflect American ideals of inclusivity rather than institutionalizing exclusion. After describing the theoretical and intellectual underpinnings of his argument, Infanti offers two comparative case studies, examining the treatment of housing tax expenditures and the unit of taxation in the United States, Canada, France, and Spain to show how tax law reflects its social and cultural context. Then, drawing on his own work and that of other critical tax scholars, Infanti explains how the discourse surrounding tax reform masks the many ways that the American tax system rewards and reifies privilege. To counter this, Infanti urges us to work together to create a society with a tax system that respects and values all Americans.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".