A Comparison of Three Workload Methodologies: POP, IP/PCT, and POPIP
Bibliographic record
Abstract
This paper examines three of the Integrated Performance Modelling Environment's built-in workload methodologies: Prediction of Operator Performance (POP), Information Processing/Perceptual Control Theory (IP/PCT), and POPIP. The newly-implemented POPIP is compared to its forerunner methodologies, POP and IP/PCT. POP, developed by QinetiQ, predicts performance degradation from interference between concurrent tasks using estimates of workload on different channels. IP/PCT, developed by DRDC Toronto, theorizes that all factors that impact human cognitive workload can be reduced to their effects on the amount of information to be processed, and the amount of time available before the task must be completed. POPIP uses components from both POP and IP/PCT for a combined workload algorithm that offers interference based on time pressure, and task scheduling. A sample IPME model is discussed, and used as an example in comparing all three workload methodologies.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".