Multiple Forces Working in Unison: The Case of Rapid Improvement of Vital Statistics in South Africa Post-1996
Bibliographic record
Abstract
In a period of about five years, from 1997 to 2002, South Africa remarkably improved the coverage and production of its vital statistics. This period witnessed the entrance of South Africa into the select league of countries that publish statistics on multiple causes of death and that make use automatic coding of causes of death. These achievements were accomplished through multiple forces working in unison. Some of the important factors contributing to the achievement were lessons learned from study tours to Australia, Sweden and the U.S.A. The paper describes these lessons and how they were adapted to suit the South African reality. Comparison is made between the status of demographic statistics by the end of apartheid and in the post-apartheid era. Stakeholder relationships that shaped the transformation of demographic statistics in the new South Africa are also discussed.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".