Identifying and Managing Risk at Work Emerging Issues in the Context of Globalisation
Bibliographic record
Abstract
With a focus on five major regions globally (UK, US, Europe, Canada, and Australia) Identifying and Managing Risk at Work outlines key regional factors affecting risk and its management.\n\nThis volume looks at the social production and social construction of risk as well as taking a labour-process approach and socio-political perspective to investigate the nature and causes of work-related risk. In addition, there are several issues included that contribute to identifying risk at work such as climate change, the "gig" economy and the "Me Too" movement. Readers will gain a picture of some of the major current issues that are affecting risk under globalisation.\n\nDrawing on these key aspects of risk, students, academics, practitioners, and policy-makers will gain a better understanding of how risk is conceptualised and identified, and of the roles of management and employees in dealing with risk. This book will be of interest to researchers and practitioners to help gain an understanding of risk for a number of regions, and how several current issues in globalisation can be seen in their risk context.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".