Performance on a Time Restricted Task: Comparing Procrastinators to Non-Procrastinators
Bibliographic record
Abstract
Previous research has shown that procrastinators do not work as well imder pressure as non-procrastinators. The present study set out to support this hypothesis. Under a time restricted task, it was hypothesized procrastinators would not perform as well as non-procrastinators. There were 40 participants that completed the study. Each of the participants completed the Adult Inventory of Procrastination and were then presented with the time restricted task. Performance was determined by speed and accuracy. The results showed that non-procrastinators completed more items (speed) but they made more errors (accuracy) then procrastinators. Therefore the original hypothesis was not fully supported. Procrastination comes from the Latin word procrastinare, meaning to put off or postpones until another day (Desimone, 1993). A recent study found that 75 % of university students procrastinate (Steel, 2007). There are various theories as to why people do procrastinate. One of which is avoidance model of academic procrastination (Rothblum, 1990). It is said that individuals procrastinate because they are afraid of
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".