Procrastination and the Priority of Short‐Term Mood Regulation: Consequences for Future Self
Bibliographic record
Abstract
Abstract Procrastination is a common and pervasive problem associated with a range of negative outcomes across a variety of life domains that often occurs when people are faced with tasks that are seen as aversive. In this paper, we argue that as a form of self‐regulation failure, procrastination has a great deal to do with short‐term mood repair and emotion regulation. Moreover, we contend that a temporal understanding of self and the mood‐regulating processes involved in goal pursuit is particularly important in understanding procrastination, because the consequences of procrastination are typically borne by the future self. After summarizing the research on the priority of short‐term mood regulation in procrastination, we then draw the connection between the focus on short‐term mood repair and the temporal disjunction between present and future selves. We present research that exemplifies these intra‐personal processes in understanding temporal notions of self characterized by procrastination, and then link these processes to the negative consequences of procrastination for health and well‐being. We conclude with a discussion of possible avenues for future research to provide further insights into how temporal views of the self are linked to the dynamics of mood regulation over time in the context of procrastination.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".