Unbreakable Resolutions as an Effective Tactic for Self-Control: Lessons From Mahatma Gandhi and a 19th-Century Prussian Prince
Bibliographic record
Abstract
Despite the relative consensus in the self-management literature that personal resolutions are not an effective stand-alone tactic for self-control, some individuals seem capable of using them to exert a remarkable level of control over their behavior. One such individual was Mahatma Gandhi, the famous Indian statesman. Gandhi often used personal resolutions—or “vows”—to commit himself to a range of challenging behaviors, such as extreme diets, sexual abstinence, and fasting. Similarly, Prince Pückler-Muskau, a celebrated 19th-Century adventurer, landscape designer and travel author, described using personal resolutions to unfailingly accomplish numerous tasks in his everyday life. In this article, we examine the historical writings of Gandhi and Pückler-Muskau concerning their use of resolutions. We describe three defining characteristics of their resolutions, which we will refer to asunbreakable resolutions, and outline Gandhi’s advice for making and keeping such resolutions. Our analysis suggests that the effectiveness of unbreakable resolutions may be primarily due to the temporally extended contingencies of reinforcement associated with their use, and can be usefully interpreted from the perspective of delay-discounting and say-do correspondence models of self-control. The implications of this examination for understanding the concept of willpower and for enhancing modern research into self-control training are also discussed. Based on this analysis, we additionally offer a tentative set of guidelines on how to make and keep unbreakable resolutions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| 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".