New and Emerging Occupational Risks (NER) in Industry 4.0: Literature Review
Bibliographic record
Abstract
The objective of this article is to identify trends in publications about new and emerging labor risks (NER) in the context of the evolution of the industry to analyze what is known so far of the subject, what has been investigated and which aspects remain unknown. Thematically is analyzed existing information on the evolution of occupational risks depending on the advancement of technology in manufacturing, considering keywords and the combination thereof, in Spanish and English during the period 2000-2019 in different sources of consultation such as magazines, articles, degree papers and conference proceedings. With this review it has been possible to identify a relationship between technological progress and a tendency to develop new and emerging labor risks in the industrial field; as a result, researchers from countries such as Spain, Germany, England, Canada and India have collaborated in their identification and have proposed different methods of measurement, which contribute to be frames of reference for future research. The effects that digitalization is having in the industry, are manifested through the so-called Industry 4.0, with technologies such as the Internet of Things, information in the cloud, Big Data, simulation, etc., and are revolutionizing the functioning of the industry, which in turn and given the organizational nature tends to change workplaces as well as the processes and practices of conventional work, creating traditional occupational hazards and other designated by the Occupational Safety and Health Administration (for its acronym in English OHSA) as new and emerging labor risks (NER).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".