Fragmentation in the Future of Work: A Horizon Scan Examining the Impact of the Changing Nature of Work on Vulnerable Workers
Bibliographic record
Abstract
Abstract Background. The future of work is characterized by social, technological, economic, environmental and political changes that are expected to disrupt all aspects of the working world. Our study aims to understand how the future of work impacts vulnerable workers. Methods. We conducted a horizon scan to systematically identify and synthesize diverse sources of evidence including academic research, gray literature and social media. Search terms were generated by members of the multidisciplinary research team, and combined with work outcome, future- and change-related and vulnerable worker search terms. Six search portals were used to uncover peer reviewed and gray literature across diverse disciplines. Search terms were also entered into Twitter’s standard search interface to identify social media resources. Literature was screened for eligibility (i.e., English language, documented a change in the nature of work, industrialized context and description of impact to vulnerable workers). Each relevant article was synthesized, and trend categories were developed by through iterative discussions among the research team. Results. An initial search yielded 4,800 articles after removing duplicates. Following a title and abstract relevancy screen, 3,195 articles were excluded. A total of 342 articles were fully reviewed. A synthesis of articles found nine trend categories which included digital transformation of the economy, artificial intelligence (AI)/machine learning (ML)-enhanced automation, AI-enabled human resource management systems, skill requirements for the future of work; globalization 2.0, 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 COVID-19 example). Some workers may be more likely to experience vulnerability in the future of work including greater exposure to job displacement or wage depression. However, some potentially positive future of work trends also existed and could be beneficial for the labor market engagement of certain groups. Discussion. The changing nature of work can be fragmented for different groups of workers. Our research offers an important step towards understanding and supporting the involvement of vulnerable workers in the future of work.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.039 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".