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Record W1994406776 · doi:10.1177/154193120004403301

Ergonomics in China: Perspectives from the 1998 People to People International Ergonomics Delegation, October 17–31

2000· article· en· W1994406776 on OpenAlexaff
Ken Page, Denny Ankrum, Lynda Enos, Margot Fraser, Shamir Jamal, Christine Demen Meier, Ian Noy

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2000
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsTransport CanadaNortel (Canada)
Fundersnot available
KeywordsDelegationChinaHuman factors and ergonomicsWorkloadGovernment (linguistics)People's RepublicEngineeringPoison controlPublic relationsPolitical scienceBusinessManagementMedicineEnvironmental healthLawEconomics

Abstract

fetched live from OpenAlex

In October 1998 nine ergonomists from around the world embarked on a two-week tour of the People's Republic of China. This delegation represented only the second formal delegation to visit this vast and largely unknown nation. The group travelled as part of a larger delegation of Citizen Ambassadors with the People to People International organization from the U.S. The Ergonomics delegation saw first hand how ergonomics is being studied and applied in China. Apart from visiting with several universities and government organizations, the group participated in the China Ergonomics Society quadrennial conference and visited several manufacturing factories. Ergonomics is not wide spread in China but the focus appears to be on cognitive ergonomics and primarily looking at mental workload capacity, macroergonomics in the global economy, and Chinese anthropometrics. As China participates more on the global stage the influence of other nations is certainly impacting the direction that ergonomics is taking in China.

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)0.005
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.004
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicErgonomics and Human FactorsFrench-language works237,207