Bibliographic record
Abstract
Abstract The study of occupational and structural changes in employment has garnered significant attention within the social sciences since the 1970s, driven by early contributions from scholars like Daniel Bell and Harry Braverman, who explored the implications of transitioning to a post-industrial society and the impact of technology on labour, respectively. Since then, particularly from the 1990s onward, the field has expanded, focusing on how technological, organizational and other transformations affect workers based on their skill level or the task content of their jobs, with the common goal of understanding how employment structures evolve over time. This book contributes to this body of literature by pursuing three main objectives. First, it updates the international evidence on occupational change using the most recent data available, covering approximately two decades up to 2022, including the impact of the COVID-19 pandemic on employment structures. Second, it broadens the scope of analysis beyond the traditionally studied regions (the US and Europe) to include a wider set of countries, encompassing also other developed and developing nations such as Canada, Mexico, Brazil, Chile, Argentina, India, Russia and South Korea. This expanded coverage allows for a more comprehensive understanding of the global mechanisms driving employment transformations, which can vary significantly across different contexts. Third, the book seeks to produce comparable evidence on a global scale by applying consistent methodologies across all countries studied, thereby minimizing the risk of discrepancies arising from methodological differences rather than actual variations in employment trends.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.072 | 0.031 |
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".