Motivated Counterfactual Thinking and Moral Inconsistency: How We Use Our Imaginations to Selectively Condemn and Condone
Bibliographic record
Abstract
People selectively enforce their moral principles, excusing wrongdoing when it suits them. We identify an underappreciated source of this moral inconsistency: the ability to imagine counterfactuals, or alternatives to reality. Counterfactual thinking offers three sources of flexibility that people exploit to justify preferred moral conclusions: People can (a) generate counterfactuals with different content (e.g., consider how things could have been better or worse), (b) think about this content using different comparison processes (i.e., focus on how it is similar to or different than reality), and (c) give the result of these processes different weights (i.e., allow counterfactuals more or less influence on moral judgments). These sources of flexibility help people license unethical behavior and can fuel political conflict. Motivated reasoning may be less constrained by facts than previously assumed; people’s capacity to condemn and condone whom they wish may be limited only by their imaginations.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".