A Change of Pace: A Latent Profile Analysis of Pacing Styles at Work
Bibliographic record
Abstract
When working towards a deadline, individuals allocate their effort over time in a multitude of ways, referred to as pacing style. The different styles that employees use to pace their work to meet deadlines have important implications for individual work outcomes, most notably performance. To date, researchers have largely focused on pacing styles in isolation, assuming that individuals have a dominant style that they use for all deadlines. However, individuals may use a combination of pacing styles for different tasks or deadlines. This consideration is important because the specific combination of pacing styles an individual uses provides a fuller picture of how they allocate time and effort as they approach deadlines. In an effort to advance the understanding of pacing style, we present two studies that use latent profile analysis to identify profiles of pacing styles and reveal how combinations of styles relate to work-related antecedents (e.g., role overload, work boredom) and outcomes (e.g., task performance, creative performance, emotional exhaustion). Our results reveal that combinations of pacing styles can provide a more nuanced understanding of how individuals distribute their effort over time in working toward deadlines and its impact in the workplace.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".