Bibliographic record
Abstract
It is increasingly acknowledged that data availability plays a crucial role in the fight against poverty.Poverty data has increased in both quantity and frequency over the past 30 years, but still lags behind the data available on most other economic phenomena.Yet there are vibrant experiences that are often overlooked:Ø Data for monetary & multidimensional poverty dramatically increased since 1980.Ø Sixty countries already produce annual updates to key statistics.Ø Some have continuous household surveys with cost-cutting synergies.Ø International agencies have probed short surveys for comparable data. Ø Certain regions have agreed on harmonised variable definitions across countries.Ø New technologies can drastically reduce lags between data collection and analysis.The post-2015 agenda identified the need for regularly updated data to monitor the Sustainable Development Goals (SDGs).This paper points out existing experiences that shed light on how to break the cycle of outdated poverty data and strengthen statistical systems.Such experiences show that it is possible to generate and analyse frequent and accurate poverty data that energizes and enables poverty eradication.
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 | 0.125 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".