Measurement of Poverty, Deprivation, and Economic Mobility
Bibliographic record
Abstract
Poverty profiles and well-being: panel evidence from Germany / Andrew Clark, Conchita D'Ambrosio, Simone Ghislandi -- Once poor, always poor? Do initial conditions matter? Evidence from the ECHP / Eirini Andriopoulou, Panos Tsakloglou -- Factors associated with poverty and indigence mobility in five Latin American countries / Luis Beccaria, Roxana Maurizio, Gustavo Vázquez, Manuel Espro -- The contribution of income mobility to economic insecurity in the US and Spain during the Great Recession / Olga Cantó, David Ruiz -- The role of skills in understanding low income in Canada / Andrew Heisz, Geranda Notten, Jerry Situ -- Immigrant child poverty: the Achilles Heel of the Scandinavian welfare state / Taryn Galloway, Björn Gustafsson, Peder Pedersen, Torun Österberg -- Rural poverty and ethnicity in China / Carlos Gradin -- Static and dynamic disparities between monetary and multidimensional poverty measurement: evidence from Vietnam / Van Tran, Sabina Alkire, Stephan Klasen -- Hardship, debt, and income-based poverty measures in the USA / Kathleen Short -- Modelling the joint distribution of income and wealth / Markus Jäntti, Eva Sierminska, Philippe Van Kerm
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".