Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Children constitute about one quarter of the world population. Globally, children are more likely to be poor than adults. In fact, estimates say that they are over twice as likely to be poor as adults. It is estimated that in 2017 17.5% children lived in poverty, vis à vis 7.9% of adults. Child Poverty is also a widespread phenomenon, in low as in high income countries, Child poverty has many long-lasting consequences on children’s lives and future opportunities. Additionally, poor children are more vulnerable to shocks of various kind, including shocks from extreme weather and natural disasters, and from conflict and violence. Measuring child poverty is therefore of crucial importance to implement effective policies. Monetary poverty provides the obvious tool to measure child poverty. However, this does not come without challenges, as monetary aggregates are calculated at the household level, and disregard intra-household inequalities. Moreover, equivalence scales conventionally used to calculate monetary poverty can substantially underestimate child poverty. Accompanying monetary with multidimensional measures of child poverty can provide a more comprehensive and realistic picture of children’s welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it