Non-psychological Weakness of Will: Self-control, Stereotypes, and Consequences
Bibliographic record
Abstract
Recently philosophers have debated which theory best captures the ordinary concept of weakness of will. Some claim that weakness of will consists in action contrary to an agent's better judgments, while others claim it consists of action contrary to an agent’s intentions. In this paper, we show that the psychological focus on violated commitments — whether judgments, intentions, or both — is too narrow. We begin by showing that many people attribute weakness of will even in the absence of a violated commitment (Experiment 1). We then show that weakness of will attributions are sensitive to two important non-psychological factors. First, for actions stereotypically associated with weakness of will, the absence of certain commitments often triggers weakness of will attributions (Experiments 2-4). Second, and in line with other recent findings, the quality of an action’s outcome affects the extent to which an agent is viewed as weak-willed. More specifically, actions with bad consequences are more likely to be viewed as weak-willed (Experiment 5). So the ordinary concept of weakness of will is sensitive to two non-psychological factors and is thus broader than previous philosophical accounts have recognized. To explain our findings, we propose a two-tier model of weakness of will as a failure of self-control.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".