Mobilising the Household Data Required to Progress toward the SDGs
Bibliographic record
Abstract
Progress on the Sustainable Development Goals (SDGs) will be reported annually, but at present data on poverty-related SDGs are not updated frequently, nor are the data always available promptly. This paper reviews the key non-census data sources underlying the MDGs -household surveys (national and international), and administrative and registry data -to assess which data sources could provide the more frequent data required to design and coordinate policies, measure, manage, and monitor progress towards the poverty-related SDGs. It also reviews new data sources such as opinion polls 'big data', satellite data, call records, and other digital breadcrumbs to see how these might augment the information required to assess progress in the SDGs. We evaluate each option according to ten criteria. While each option has strengths, and each will clearly contribute, high quality multi-topic household surveys complemented by interim lighter surveys have a demonstrated ability to collect the core indicators of human poverty at an individual and household level in a rigorous way, so are likely to remain a core component of the data framework.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".