Charting the future of censuses: Insights, lessons and recommendations for the 2030 round
Bibliographic record
Abstract
Population censuses globally remain the primary source of official statistics despite the existence of sample surveys and administrative data sources, like population registers. The 2020 round of censuses was predominantly characterised by traditional approaches in about 69% of the countries, where data was obtained directly from respondents regardless of the push to explore alternative sources compelled by COVID-19. From the Babylonian times in 3800 BC to date, the principal purpose of a census has been to foster public administration. While the 1666 census in New France (now Quebec) marked the first-ever scientifically sound enumeration, it still fell short of what presently typifies a census. Besides, lack of globally standardised methods dwarfed the acceptability and comparability of results, leading to harmonisation efforts and the gradual adoption of modern censuses from the mid-1800s. Subsequently, the United Nations developed the maiden international standards on population censuses soon after World War II and established the decennial World Population and Housing Census Programme. Overtime, the census has evolved to what globally embodies universality, individual enumeration, simultaneity, periodicity and capacity to produce small area statistics. As countries transition towards the 2030 round, this paper reviews the global developments, lessons, and provides recommendations for future census implementation.
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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 | 0.041 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".