Bibliographic record
Abstract
How do electoral rules affect the poor? Under what conditions are legislators likely to be more or less responsive to the poor? This research departs from current explanations of cross-national differences in social policy by recognizing that antipoverty measures are well-suited for manipulation by re-election-motivated legislators: Antipoverty measures are highly targeted policies that are readily perceived by the beneficiaries and can be directly attributed to incumbent legislators. In combination with the geographic distribution of income groups, electoral rules determine the electoral power of low-income citizens, and structure legislators’ incentives to be responsive to this constituency. The generosity of antipoverty measures will reflect the share of legislators that rely on low-income citizens’ electoral support -- an intuition that is developed in a series of formal-analytic examples that demonstrate the important modifying effect of electoral geography on the more general relationship between electoral rules and social policy. Support for this election-motivated account of antipoverty policy is presented two forms: First, I take full advantage of Italy’s electoral reform and the dramatic change in Germany’s electoral geography following re-unification to demonstrate that improvements in the electoral power of the poor are followed by increases in the effectiveness of antipoverty measures; Chapter 4 reports the results of this analysis. Second, in a broadly comparative analysis, Chapter 5 establishes the general – positive – relationship between the electoral power of a low-income voting bloc (i.e., the number of seats elected by low-income citizens, if they all turn out to vote, and all vote for the same party), and levels of targeted poverty relief. Both of these analysis use a new measure of poverty responsiveness, developed in Chapter 3, as their dependent variable. The poverty relief ratio, R, assesses the effectiveness of antipoverty transfers from the perspective of low-income citizens. That cross-national and over-time differences in levels of poverty relief reflect variation in legislators’ electoral incentives to be responsive to low-income citizens is both surprising and a cause for concern: The electoral institutions of contemporary democratic societies may undermine opportunities for these societies to fulfill their obligations to democratic equality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".