The Future of Work and Workers: Insights from US Labour Studies
Bibliographic record
Abstract
The rollout of sophisticated digital tools -including advanced robotics, data analytics, machine learning and the Internet of Things -threatens to disrupt the distribution, role and nature of work in society. Raising the spectre of mass unemployment and social instability, researchers predict that technological progress will soon allow for the rapid automation of many tasks that are currently performed by humans. Already the pace of change appears to accelerate, with the spread of platform-based business models fuelling the growth of gig and crowd work. While reductions in labour supply due to demographic shifts and COVID-19 militate against mass displacement, the prospects for the offshoring of services enabled by information technology (IT) and even the most limited applications of artificial intelligence (AI) will challenge inherited divisions of labour across societies Most workers, including those far up the skills ladder and those in high-status jobs, will experience some form of disruption to their work duties. 1 Concurrently, other trends such as climate change, financialisation and workplace fissuring threaten to accelerate the ongoing concentration of power across societies in the hands of the wealthy few, leaving workers with less bargaining power and greater uncertainty.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".