How much workload is a ‘good’ workload for human beings to meet the deadline: human capacity zone and workload equilibrium
Bibliographic record
Abstract
When given a ‘good’ workload, human participants can efficiently complete the assigned task within the time limit, while they may fail to complete it due to low efficiency when given a ‘bad’ workload. The objective of this research is to investigate how much workload is considered ‘good’ for individuals to meet a deadline and successfully complete the assigned task. High work efficiency can be achieved by manipulating workload assignments and assigning them to different individuals at the appropriate time. We have defined the range of this ‘good’ workload as the capacity zone, which should be supported by necessary interventions from computers or human instructors. The capacity zone represents the area between the two workload equilibrium points, whose position and shape are influenced by factors such as mental capacity, maximum efficiency, and stress limit. Our analysis and simulation results indicate that humans are only capable of effectively completing a large amount of workload assignment by the deadline when working within their capacity zone. Therefore, this research aims to enhance overall work efficiency by customising workload allocation strategies based on different individuals' capacity zone and providing timely intervention when they are working beyond their capacity zone.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".