For a Future of Work with Dignity: A Critique of the World Bank Development Report, The Changing Nature of Work
Bibliographic record
Abstract
Technological change has brought about rapid changes in the world of work over the past decade. The World Bank’s World Development Report 2019: The Changing Nature of Work is a welcome contribution as it discusses the transformations that are taking place and tries to advise governments on how best to adapt to them. The report also brings out the concern related to the growing risks associated with tax evasion by large corporations that control the market power and have an ever-greater share of economic activity. However, the report is flawed in many ways as it portrays these changes in the nature of work as essentially benign, requiring “adaptation” and skills acquisition by workers facilitated by the provision of skills and “universal” social coverage by governments, with the latter understood as a prelude to labour-market deregulation. Such a narrow perspective ignores the growing body of research that points to very serious risks and challenges faced by workers in ensuring decent working conditions due to technological changes. This article provides a critique of the World Bank report by focusing on five areas related to technology and the future of work that are fundamental for ensuring minimum standards for workers and to ensure social cohesion: inequality, jobs, labour regulations, trade unions and social protection. KEYWORDS future of work; technology; inequality; jobs; labour regulation; trade unions; social protection
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.024 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".