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Record W2789174123 · doi:10.5271/sjweh.3718

Scientific basis of ISO standards on biomechanical risk factors

2018· article· en· W2789174123 on OpenAlexaff
Tom J Armstrong, Alex Burdorf, Alexis Descatha, Andrea Farioli, Maggie Graf, Seichi Horie, William S. Marras, Jim R. Potvin, David Rempel, Giovanna Spatari, Esa‐Pekka Takala, Jos Verbeek, Francesco Saverio Violante

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProcess (computing)Value (mathematics)International standardSafety standardsProfessional standardsBusinessRisk analysis (engineering)Engineering ethicsEngineeringComputer science

Abstract

fetched live from OpenAlex

Among other purposes, companies and regulatory agencies from around the world often adopt International Standard Organization (ISO) standards to determine acceptable practices, equipment and criteria for preventing occupational injuries and illnesses. ISO standards are based on a consensus among individuals who participate in the process. This discussion paper examines the scientific process for the development of several ISO standards on biomechanical factors, comparing it with processes used by other professional organizations, including scientific committees working on the development of clinical guidelines. While the ISO process has value, it also has clear limitations when it comes to developing occupational health and safety standards that should be based on scientific principles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.100
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1000.152
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.006
Science and technology studies0.0030.009
Scholarly communication0.0070.004
Open science0.0050.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.063
GPT teacher head0.429
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2018
Admission routes1
Has abstractyes

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