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 (Autor, 2015;Baldwin, 2016).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.Given these developments, it should be no surprise that anxiety about the future runs high.In the United States (US), this has translated into more diverse and more contentious political debates.On the one hand, new visions for pooling collective risk, including the introduction of universal minimum income schemes, have entered mainstream thinking.Yet, on the other hand, policy-makers often continue with long-running efforts to undermine the fiscal power of the state, on which such new policy schemes would rely.Moreover, as economic inequality has grown and younger cohorts' prospects have dimmed, elites have taken more assertive steps to ensure against downward social mobility.Private investments in academic credentials have been a central means for transferring privilege from one generation to the next, whether pursued within or outside of increasingly stratified public education systems, and with the frequent tendency of weakening public provision.At the same time, sections of the population experiencing status erosion have begun to express their grievances in more forceful -and at times violent -ways.Just when mastering the looming socio-economic transformation requires effective mechanisms for collective action, the public approval of societies' central political-economic institutions has fallen, from Congress and the Presidency to Corporate America.Sadly, this is more than justified, given that even mainstream scholarship has found "substantial support for theories of Economic-Elite Domination and … Biased Pluralism" (Gilens and Page, 2014: 564).At the same time, public support for unions is at an all-time high in the United States, according to recent
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".