Revisiting the Profile of the American Voter in the Context of Declining Turnout
Bibliographic record
Abstract
The phenomenon of declining voter turnout in U.S. national elections has been one of the major perplexing issues that political scientists have attempted to explain in recent decades. Today we are face to face with a participation rate that has fallen nearly one-quarter of its initial value since 1960. My article has aimed at redrawing the profile of the American voter in the second half of the 20 th century. Reliable data for the period of 40 years presented a valuable opportunity to add to the picture of the turnout phenomenon in the tradition of a behavioral approach. In the first part of this work I have tested test the notion that the overall level of life satisfaction affects the individual’s decision whether or not to participate in elections. Known to be directly related to the well-being of its citizens, the economic performance of the entire state was another criterion to be tested as to its effect on the voter turnout over last 40 years. Hence, in this section, I have checked for the impact of macroeconomic indicators such as the minimum wage, unemployment, and inflation rates, as well as the announced percentage of the population temporarily receiving financial assistance from the government. Next, I referred to societal factors and analyzed whether the sense of insecurity or the level of crime has discouraged people to vote. Finally, concerning institutional factors, I measured the changes in the overall turnout since 1960, controlling for an increased population due to foreign-born immigrants. Test results support the general wisdom about political participation in the period in question and lead us to look for causes in the traditional literature, particularly in partisanship ties, schemas, and candidate evaluations. The life-satisfaction of people and the origins of the ‘added’ population were shown to have had no real effect on turnout. ‘Economic variables’ failed to explain the phenomenon as well. Crime rates and the assistance for needy families, however found some empirical support. People become more dissociable as they get frightened; thus they participate less. The repercussions of tax payers, however, were dominant in government policies regarding assistance for
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".