Sex, Education and Procrastination: An Epidemiological Study of Procrastinators’ Characteristics from A Global Sample
Bibliographic record
Abstract
Procrastination is a common form of self–regulatory failure with substantive connections to lower levels of health, wealth and well–being. Conducting an epidemiological study, we determined the characteristics of prototypical procrastinators from a global sample based on several relevant self–reported demographic variables. Using an internet sampling strategy, we surveyed 16 413 English–speaking adults (58.3% women; 41.7% men: M age = 38.3 years, SD = 14), specifically on the variables of sex, age, marital status, family size, education, community location, and national origin. Almost all the results were statistically significant because of our large sample size. However, procrastination tendencies were most prominently associated with sex, age, marital status, education and nationality. Procrastinators tended to be young, single men with less education, residing in countries with lower levels of self–discipline. Notably, procrastination mediated the relationship between sex and education, providing further support that men are lagging behind women academically because of lower self–regulatory skills. Given procrastination's connection with a variety of societal ailments (e.g. excessive debt, delayed medical treatment), identifying risk factors and at risk populations should be helpful for directing preventative public policy. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".