Obama and 2012: Still a Racial Cost to Pay?
Bibliographic record
Abstract
Abstract Will President Obama lose votes in 2012 because of racial prejudice? For 2008, we estimated, via a carefully controlled, national survey-based study, that on balance he lost about five percentage points in popular vote share due to intolerance for his race on the part of some voters. What about 2012? There are at least three possibilities: (1) the presidency has become postracial, and the vote will register no racial cost; (2) intolerance has increased, and the vote will register an increased racial cost; and (3) intolerance has decreased, and the vote will register a decreased racial cost. Our evidence, drawn from an analysis comparable to that carried out for 2008, suggests Obama will pay a racial cost of three percentage points in popular vote share. In other words, his candidacy will experience a decrease in racial cost, if a small one. In 2008, this racial cost denied Obama a landslide victory. In the context of a closer election in 2012, this persistent racial cost, even smaller in size, could perhaps cost him his reelection.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".