Fragmentation in the future of work: A horizon scan examining the impact of the changing nature of work on workers experiencing vulnerability
Bibliographic record
Abstract
INTRODUCTION: The future of work is characterized by changes that could disrupt all aspects of the nature and availability of work. Our study aims to understand how the future of work could result in conditions, which contribute to vulnerability for different groups of workers. METHODS: A horizon scan was conducted to systematically identify and synthesize diverse sources of evidence, including academic and gray literature and resources shared over social media. Evidence was synthesized, and trend categories were developed through iterative discussions among the research team. RESULTS: Nine trend categories were uncovered, which included the digital transformation of the economy, artificial intelligence (AI)/machine learning-enhanced automation, AI-enabled human resource management systems, skill requirements for the future of work; globalization 4.0, climate change and the green economy, Gen Zs and the work environment; populism and the future of work, and external shocks to accelerate the changing nature of work. The scan highlighted that some groups of workers may be more likely to experience conditions that contribute to vulnerability, including greater exposure to job displacement or wage depression. The future of work could also create opportunities for labor market engagement. CONCLUSION: The future of work represents an emerging public health concern. Exclusion from the future of work has the potential to widen existing social and health inequities. Thus, tailored supports that are resilient to changes in the nature and availability of work are required for workers facing vulnerability.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".