Goal Disengagement in Everyday Life: Longitudinal Observation of New Year’s Resolutions
Bibliographic record
Abstract
Goal disengagement has been well studied in contexts where giving up is generally adaptive, and understudied in more ordinary situations. 1,201 American adults described up to five New Year’s resolutions and reported on their goal pursuit after six months and one year. Explicit goal disengagement was very rare and occurred in less than 7% of goals at six months and one year. More often, people took breaks and discontinued pursuit (e.g., simply devoting no effort and commitment to the goal, not often or recently working on the goal). People did not often make a deliberate decision to quit, but for nearly one quarter of goals, people thought about it. People who scored higher in a measure of self-regulatory skill (Trait Self-Control) tended to discontinue pursuit less often. There was not evidence that when they did, they felt better about it than their less-skilled counterparts. This research documents phenomena that fall between quitting and persistence. In doing so, it highlights the value of studying goal phenomena in everyday contexts, and the need for theoretical and empirical work that clarifies the defining qualities and processes of goal disengagement and adjacent phenomena as they occur in the context of people’s genuinely held goals.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".