The Government Taketh Away: The Politics of Pain in the United States and Canada.
Bibliographic record
Abstract
The Government Taketh Away: The Politics of Pain in the United States and Canada., Leslie A. Pal and R. Kent Weaver, eds., Washington, DC: Georgetown University Press, 2003, pp. xii, 340. Compiling edited collections is notoriously difficult because editors and contributors frequently work from a different script. The result is that instead of producing a coherent volume which addresses a particular theme, readers are often left with a collection of scholarly papers that share little in common. What may have started as a project with a single goal and focus can quickly disintegrate into a patchwork quilt. This major problem has been avoided in Leslie Pal and Kent Weaver's edited book, The Government Taketh Away: The Politics of Pain in the United States and Canada, a sophisticated and richly detailed analysis of how decision-makers in the two countries attempt to introduce policies that may adversely affect the economic, social and political interests of various groups while trying to minimize political fallout. As the title of this book suggests, the editors are not concerned about why policy makers reward certain sectors and groups in society. After all, common sense dictates that politicians need votes and attempt to acquire them by appealing to the broadest segment of the population. In this book, the focus is on how policy makers, when faced with potential opposition from different groups, make strategic decisions that result in the imposition of losses. Although the editors do not offer a concrete definition of loss, examples include policy decisions that result in the de-indexation of old age pensions, the closure of military bases and the retraction of tax benefits. This book is not an indictment of government—the editors acknowledge that in democracies politicians must often make difficult choices that will help some and hurt others. Rather, it is a thorough exploration of how decision makers make these decisions and how various groups and sectors react.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".