The Psychology of Procrastination: How We Create Categories of the Future
Bibliographic record
Abstract
Paying bills, filling out forms, completing class assignments, or submitting grant proposals – we all have the tendency to procrastinate. We may engage in trivial activities such as watching TV shows, playing video games, or chatting for an hour and risk missing important deadlines by putting off tasks that are essential for our financial and professional security. Not all humans are equally prone to procrastination, and a recent study suggests that this may in part be due to the fact that the tendency to procrastinate has a genetic underpinning. (2) Yet even an individual with a given genetic makeup can exhibit a significant variability in the extent of procrastination. A person may sometimes delay initiating and completing tasks, whereas at other times that same person will immediately tackle the same type of tasks even under the same constraints of time and resources. \n\nA fully rational approach to task completion would involve creating a priority list of tasks based on a composite score of task importance and the remaining time until the deadline. The most important task with the most proximate deadline would have to be tackled first, and the lowest priority task with the furthest deadline last. This sounds great in theory, but it is quite difficult to implement. A substantial amount of research has been conducted (3) to understand how our moods, distractability, and impulsivity can undermine the best-laid plans for timely task initiation and completion. The recent research article The Categorization of Time and Its Impact on Task Initiation (4) by the researchers Yanping Tu (University of Chicago) and Dilip Soman (University of Toronto) investigates a rather different and novel angle in the psychology of procrastination: our perception of the future.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".