Generational income mobility in North America and Europe: an introduction
Bibliographic record
Abstract
D uring the 1990s, a number of countries in both North America and Europe set explicit targets for the reduction of child poverty, including the United Kingdom, Ireland, and Canada. In the United Kingdom, the pledge, announced in 1999, was to eliminate child poverty in a generation; in Canada, the ambition, made clear a decade earlier, was to seek to do the same by the year 2000. And even in countries less explicit about their goals, reducing child poverty has been an important public policy concern. This, for example, is as true in the United States, where child poverty rates have historically been among the highest relative to other rich countries, as it is in Sweden, where they have been among the lowest. Clearly, this issue has a strong resonance in public policy discourse, and reflects a growing concern over the welfare of all children regardless of their place in the income distribution. But why should societies care more about children than any other group? One possible reason is that children have certain rights as citizens, but are dependent upon others for the defense of their rights. This may certainly be the case, but another reason – one often explicitly made by advocates – is instrumental: children should be thought of as investments in the future. This argument suggests that in the long run the productivity of the economy and the well-being of all citizens would be higher if the well-being of children were improved, and in particular if child poverty were reduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".