Disaggregating population data for assessing progress of SDGs: methods and applications
Bibliographic record
Abstract
Rapid population growth has had a significant impact on society, economy and environment, which will challenge the achievement of the United Nations Sustainable Development Goals (SDGs). Spatially accurate and detailed population distribution data are essential for measuring the impact of population growth and tracking progress on the SDGs. However, most population data are evenly distributed within administrative units, which seriously lacks spatial details. There are scale differences between the population statistical data and geospatial data, which makes data analysis and needed research difficult. The disaggregation method is an effective way to obtain the spatial distribution of population with greater granularity. It can also transform the statistical population data from irregular administrative units into regular grids to characterize the spatial distribution of the population, and the original population count is preserved. This paper summarizes the research advances of population disaggregation in terms of methodology, ancillary data, and products and discusses the role of spatial disaggregation of population statistical data in monitoring and evaluating SDG indicators. Furthermore, future work is proposed from two perspectives: challenges with spatial disaggregation and disaggregated population as an Essential SDG Variable (ESDGV).
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".