A moral house divided: How idealized family models impact political cognition
Bibliographic record
Abstract
People's political attitudes tend to fall into two groups: progressive and conservative. Moral Politics Theory asserts that this ideological divide is the product of two contrasting moral worldviews, which are conceptually anchored in individuals' cognitive models about ideal parenting and family life. These models, here labeled the strict and nurturant models, serve as conceptual templates for how society should function, and dictate whether one will endorse more conservative or progressive positions. According to Moral Politics Theory, individuals map their parenting ideals onto the societal domain by engaging the nation-as-family metaphor, which facilitates reasoning about the abstract social world (the nation) in terms of more concrete world experience (family life). In the present research, we conduct an empirical examination of these core assertions of Moral Politics Theory. In Studies 1-3, we experimentally test whether family ideals directly map onto political attitudes while ruling out alternative explanations. In Studies 4-5, we use both correlational and experimental methods to examine the nation-as-family metaphor's role in facilitating the translation of family beliefs into societal beliefs and, ultimately, political attitudes. Overall, we found consistent support for Moral Politics Theory's assertions that family ideals directly impact political judgment, and that the nation-as-family metaphor serves a mediating role in this phenomenon.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".