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Record W2603420535 · doi:10.3138/jcfs.34.3.311

Introduction: Dimensions of Children’s Inequality

2003· article· en· W2603420535 on OpenAlexaffvenue
Elizabeth Fussell, Anne H. Gauthier

Bibliographic record

VenueJournal of Comparative Family Studies · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInequalitySocial inequalitySociologyEconomicsDemographic economicsMathematics

Abstract

fetched live from OpenAlex

INTRODUCTIONIn the last third of the 20th century, children's special protected status has been affirmed by nearly every nation (UNICEF 2002). Yet throughout the world, are still more likely to live in poverty than are adults (European Commission 2001). Some national governments have attempted to reduce and eliminate childhood poverty. In 1964 the American President Johnson launched 'War on Poverty.' In 1975 the European Commission created its first European Poverty Program, and the British Labour Party under the leadership of Tony Blair took child poverty pledge in 2001, aiming at bringing another million out of poverty in the UK by 2005 (Lemann 1998; Gauthier 1996; BBC 2001). The United Nation's Convention on the Rights of the Child had been signed by every nation, signifying their intention to eliminate child poverty and create a world fit for children (UNICEF 2002a). Despite these political efforts, poverty still affects one child out of four in the United States, and one child out of five in the United Kingdom. Globally, the World Bank estimated that 1.2 billion people world-wide were living in poverty in 1998 - large proportion of which was (World Bank 2002).Poverty is, however, only one dimension of inequality. In recent decades, the political discourse has endorsed larger definition of inequality, going beyond monetary definition of poverty, and referring more broadly to the right to health and education, and equal social participation. For example, the United Nations and other organizations have called for the elimination of discrimination against children, as well as women and girls, and the elimination of racial discrimination - all obvious forms of inequality (UNFPA 2000; UN 2001 ; UNICEF 2002a). And the European Union has mandated the elimination of social exclusion as part of its 1999 Treaty of Amsterdam (EU 2002). The mandate to combat these inequalities is broadly construed, including addressing such problems as unemployment, low levels of education, school drop-out, and occupational training, low incomes, poor housing, high crime rates, poor health and family breakdown (UK Social Exclusion Unit 2001 ; UNICEF 2002a).While social scientists have long studied the economic well-being of and have in fact been responsible for bringing children's problems to the attention of politicians, they have only recently begun to measure children's status using more complex and nuanced methodologies (Mauser, Brown, and Presser 1997). Our intention in this volume is to bring together some of these new measures and methods for studying and inequality.The articles included in this special issue all examine specific dimensions of inequality among children, ranging from the traditional monetary dimension to issues of health, schooling, and public resources, and the intervening factors of family structure and resources. In this introduction to the special issue, we discuss the dimensions of inequality that serve to group the articles included in the volume - dynamic measures of income inequality, inequality in health and education, particularly by gender and ethnicity, and finally, multi-dimensional measure of social exclusion. In doing so, we intend to point out the unique insights provided . by viewing children's inequality through different lenses. We go on to discuss the policy implications suggested by these studies and others that address children, families, and inequality.MEASURING INEQUALITYWhen different dimensions of inequality are considered, measuring inequality becomes more complex. Measuring inequality has been the subject of numerous recent books and articles (Bradbury and Jantti 1999; Ringen 1988; Vleminckx and Smeeding 2001). We summarize here three approaches in an attempt to map some of the between and within-country variations, as well as locating the contribution of each paper in this special issue.INCOME-BASED ENEQUALITYChildren's economic well-being generally depends upon their parent's income. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.399
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2003
Admission routes2
Has abstractyes

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