Modelling human boredom at work: mathematical formulations and a probabilistic framework
Bibliographic record
Abstract
Purpose Boredom is believed to be the common cause of workers' absenteeism, accidents, job dissatisfaction, and performance variations in manufacturing environments with repetitive jobs. Effectively measuring and possibly predicting job boredom is the key to the design and implementation of appropriate strategies to deal with such undesirable emotional state. The purpose of this paper is to present new methodologies to measure and predict human boredom at work. Design/methodology/approach Two series of mathematical formulations, linear and nonlinear, to describe the variation of human boredom at work are first presented. Given the complexity of human emotions, the authors also present a probabilistic framework based on state‐of‐the‐art Bayesian networks to model employees' boredom at work. Findings The proposed methods centre on the prediction and measurement of human boredom at work. They enable managers to take proactive actions to deal with human boredom at work. Examples of such actions are task rotation and job redesign. Research limitations/implications The proposed methods are verified using a number of cases describing a set of phenomena that may occur in the real world. However, further research is required to demonstrate the validity of the models using real world data. Originality/value According to accessible literature, human boredom is being measured by self reporting scales thus far. This study describes and demonstrates analytical approaches to model human boredom at work.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".