Poverty across states, nations, and continents
Bibliographic record
Abstract
Introduction This chapter examines issues concerning regional variations in poverty, in particular, child poverty. That is, what difference does it make for our understanding of the situation of poverty in a country if one focuses not on the nation as a whole but on particular communities or other reference groups within the nation? In our case we are concerned especially with possible variations in child poverty rates among the 50 United States (plus the District of Colombia) as compared to variations across the nation states of the European Union (EU), for instance. To provide a broader context for this discussion we also use Luxembourg Income Study (LIS) data to make comparisons with variations by regions in two other countries – Australia and Canada2. This chapter therefore moves beyond our own and others recent studies of child poverty based on LIS data and breaks new ground (eg, see Smeeding and Torrey, 1988; Rainwater, 1990; Smeeding et al, 1990; Förster, 1993; Smeeding et al, 1995; Smeeding, 1998; Bradbury and Jänttii, 1999). Studies of poverty, particularly comparative studies, almost always take the nation as their prime focus and reference group, certainly with respect to the definition of the poverty line but also often more broadly than that. This focus on the nation is very much taken-for-granted in most countries. One would be hard put to find thorough examinations of whether the nation is the appropriate social reference group and physical unit for defining and then measuring the extent of poverty. For example, while the definition of poverty adopted by the European Community (EC) in 1994 reflects a conception of poverty grounded in an understanding of the nature of social stratification in prosperous industrial societies, it adopts without discussion the nation as the unit for defining ‘limited resources’, ‘exclusion’, and ‘minimum acceptable way of life’. The poor shall be taken to mean persons, families, and groups of persons whose resources (material, cultural, and social) are so limited as to exclude them from the minimum acceptable way of life in the member state in which they live. (Commission of the European Communities, 1994) Yet, there could be important variations in different communities within a country in how these characteristics of the standard of living are defined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".