Barriers and Bootstraps? The role of attributions for social mobility success and failure in policy support and faith in the American Dream
Bibliographic record
Abstract
Despite rising inequality making upward social mobility difficult, faith in the American Dream persists. Americans are often exposed to narratives where hard work leads to upward social mobility but are less likely to hear about the numerous instances where the same efforts don’t pay off. Across three pre-registered studies, we examined responses to identical narrative of social mobility effort that either ended in success or failure. Despite equal efforts, a target was viewed as less hardworking and competent and worse at managing their time and money when they failed vs. succeeded to be upwardly mobile. Liberals and conservatives made equally strong internal explanations for social mobility successes. However, conservatives explained failures with more internal and less societal attributions than liberals. These attributions were found to have important implications for faith in the American Dream and support for policies to promote equality. Moreover, experimentally inducing a focus on societal barriers to upward mobility (vs. internal factors) increased support for policies to reduce these barriers, and reduced faith in the American Dream, particularly among conservatives.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".