On the validity of the CNI model of moral decision-making: Reply to Baron and Goodwin (2020)
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The CNI model of moral decision-making is a formal model that quantifies (1) sensitivity to consequences, (2) sensitivity to moral norms, and (3) general preference for inaction versus action in responses to moral dilemmas. Based on a critique of the CNI model’s conceptual assumptions, properties of the moral dilemmas for research using the CNI model, and the robustness of findings obtained with the CNI model against changes in model specifications, Baron and Goodwin (2020) dismissed the CNI model as a valid approach to study moral dilemma judgments. Here, we respond to their critique, showing that Baron and Goodwin’s dismissal of the CNI model is based on: (1) misunderstandings of key aspects of the model; (2) a conceptually problematic conflation of behavioral effects and explanatory mental constructs; (3) arguments that are inconsistent with empirical evidence; and (4) reanalyses that supposedly show inconsistent findings resulting from changes in model specifications, although the reported reanalyses did not actually use the CNI model and proper analyses with the CNI model yield consistent findings across model specifications. Although Baron and Goodwin’s critique reveals a need for greater precision in the description of the three model parameters and for greater attention to properties of individual dilemmas, the available evidence indicates that the CNI model is a valid, robust, and empirically sound approach to gaining deeper insights into the determinants of moral dilemma judgments, overcoming major limitations of the traditional approach that pits moral norms against consequences for the greater good (e.g., trolley dilemma).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it