Rethinking Procrastination: Positive Effects of "Active" Procrastination Behavior on Attitudes and Performance
Bibliographic record
Abstract
Researchers and practitioners have long regarded procrastination as a self-handicapping and dysfunctional behavior. In the present study, the authors proposed that not all procrastination behaviors either are harmful or lead to negative consequences. Specifically, the authors differentiated two types of procrastinators: passive procrastinators versus active procrastinators. Passive procrastinators are procrastinators in the traditional sense. They are paralyzed by their indecision to act and fail to complete tasks on time. In contrast, active procrastinators are a "positive" type of procrastinator. They prefer to work under pressure, and they make deliberate decisions to procrastinate. The present results showed that although active procrastinators procrastinate to the same degree as passive procrastinators, they are more similar to nonprocrastinators than to passive procrastinators in terms of purposive use of time, control of time, self-efficacy belief, coping styles, and outcomes including academic performance. The present findings offer a more sophisticated understanding of procrastination behavior and indicate a need to reevaluate its implications for outcomes of individuals.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".