Globalization and the early life course A description of selected economic and demographic trends
Bibliographic record
Abstract
'The purpose of this chapter is to present some graphic materials that illustrate at the aggregate level selected points made in the introductory chapter to this volume. It is divided into 2 sections, the first on economic developments related to the globalization process, the second on demographic developments that can be seen as related to changes in economic opportunities and constraints over time. The graphic materials presented in both sections follow a similar design. That is, the 14 countries reported on in this volume are grouped into 5 ideal-typical clusters corresponding to (the expanded version of) Esping-Andersen's (1999) scheme of welfare regimes: conservative (Germany, the Netherlands, France), social-democratic (Sweden, Norway), post-socialist (Hungary, Estonia), liberal (Great Britain, Canada, United States of America), and familistic (Mexico, Italy, Spain, Ireland). Where possible, each cluster is supplemented with data on 1 or 2 additional countries that are not included in this volume but which may make it easier to generalize more broadly. The time series data to be presented for these countries are ordered either by calendar year (covering the last 5 or 2 decades of the 20th century, depending on data availability), or by birth cohort. Sources used vary from several ILO, OECD and EUROSTAT publications to country-specific FFS or other reports, as indicated at the bottom of each graph. Great care has been taken to make all data series as comparable as possible.' (author's abstract)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".