The Fiscalization of Social Policy: How Taxpayers Trumped Children in the Fight Against Child Poverty
Bibliographic record
Abstract
The Fiscalization of Social Policy offers a comparative-historical perspective on the growing use of tax expenditures for social policy purposes. McCabe primarily focuses on the policymaking experiences of the United States, Canada, and the United Kingdom, tracing differences in policy legacies to explain why the U.S., unlike its liberal counterparts, has not expanded its version of a child tax credit (CTC) to non-working families. The two puzzles underlying the basis of McCabe’s investigation make it clear why researchers of poverty and social policy ought to engage with the book’s arguments. McCabe first considers how we should understand the increasing fiscalization of welfare state expenditures. Second, he questions why CTCs in the U.S. are targeted at working families, whereas Canada and the UK have extended their tax credits to non-working families. In explaining the rise of fiscalization, McCabe convincingly points to convenient obfuscation strategies that have allowed policymakers to conceal the real costs of tax-based expansion. Policymakers across the liberal countries were able to classify tax expenditures as “revenues not collected” rather than typical social transfer expenditures. This practice served dual purposes: governments could signal to financial overseers that they were serious about cutting costs, while at the same time expanding benefits for their constituents.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".