“INCREASING WORK AND INCOME AMONG LOW-INCOME HOUSEHOLDS:
Bibliographic record
Abstract
*The authors would like to thank Karen Cimilluca and Kati Foley for their help in preparing this manuscript, and Luis Ayala for helpful suggestions. The authors thank the Luxembourg Income Study member countries, especially Spain and the Institute for Fiscal Studies for their support. The conclusions reached are those of the authors alone. Conditions of Social Vulnerability, Work and Low Income, Evidence for Spain in Comparative Perspective Social vulnerability due to insufficient income and earnings may come from many sources, both demographic and economic, in a globalizing world. This paper examines the problems of population aging, low wages, growing inequality, low work hours and insufficient social spending in Spain.Vulnerable groups such as children and the aged are considered. The paper will look at the United States, Canada, and Europe using the LIS (Luxembourg Income Study) database, and especially with a focus on Spain. For the first time we compare the similarities and differences between a set of Mediterranean LIS nations: Spain, Italy and Greece, compared to their European and OECD counterparts. We will assess the net effects of existing policies on poverty and inequality, and particularly the United Kingdom’s recent program to reduce child poverty. While best practices may be identified, each nation must create its own set of mutually supportive policies which provide protection against global economic forces while at the same time encouraging self effort and efficient behavior, especially in the labor market. In the end, policy can make a difference in outcomes, as shown by the recent British success in fighting child poverty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".