Dissecting the politics of “Obamacare”: The role of distributive justice, deservingness, and affect
Bibliographic record
Abstract
Abstract Political conservatives (vs. liberals) are commonly thought to oppose government‐based policies that promote equal distribution of resources, preferring that resources be distributed based on merit. In a sample of American adults (n = 436), distributive fairness perceptions, deservingness, and affective reactions related to the Affordable Care Act (ACA) were examined to better understand the ideological differences in ACA attitudes. Participants completed measures of political orientation, ACA knowledge and attitudes, deservingness, distributive justice principles (i.e., need, equality, merit), anger, and sympathy. Identifying as politically liberal (vs. conservative) and greater knowledge on the ACA predicted greater ACA support. Preferences for the distributive justice principles of equality and need (but not equity) mediated the relation between political orientation and ACA attitudes. Further, conservatives perceived less deservingness and in turn experienced greater anger and opposition to the ACA. Additional exploratory analyses also suggest that the positive path between deservingness and ACA support is moderated by political orientation such that it is stronger among political liberals than conservatives. Implications of the ideological chasm in relation to the ACA are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".