The Fourth International Physical Employment Standards Conference: Perspectives, Themes and Future Directions
Bibliographic record
Abstract
This special section of WORK is intended to provide researchers, practitioners, and policy makers with examples of the varying of the applications of Physical Employment Standards (PES) frameworks in physically demanding occupations. The special section is based on the Fourth International PES Conference held at Bond University on the Gold Coast, Australia, from the 24th–26th February 2023. This three-day conference was attended by researchers, practitioners and policymakers working within the military, law enforcement, fire and rescue, paramedicine, astronautics, sport, and industry sectors from 10 nations. The conference, which was delayed by 18 months due to the COVID-19 pandemic, built upon previous meetings in Canberra (Australia, 2012), Canmore (Canada, 2015), and Portsmouth (United Kingdom, 2018). The previous PES conferences provided the foundations for the further work presented at the 2023 conference, to contribute to the development of PES methodologies covering the design, development, and implementation of PES within various occupations and agencies. In addition, the fourth PES featured the expansion of the PES methodology to aid in physical conditioning and return-to-work planning for personnel and in the evaluation of job-specific assessments and personal protective equipment.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".