Deficiency in civil registration and vital statistics reporting in remote areas: the case of Sabah, Malaysia
Bibliographic record
Abstract
Abstract Malaysia has a well-established civil registration system dating back to the 1960s. Birth registration is virtually complete at the national level. However, the quality of civil registration in some remote areas is doubtful, as evidenced by the abnormally low birth and death rates in several districts. This study focuses on identifying districts in Sabah, where the reporting of births seems problematic. Sabah is the least developed state in Malaysia, and it is sparsely populated, despite being the second most populous state in the country. Sabah’s civil registration lags behind the other states, to the extent that birth and death statistics were not reported for the state in the vital statistics report for the period 2000 to 2009. A 2016 study found that death registration is almost 100%, except for Sabah (88%). The plausible reasons behind the ultra-low birth rate reported in several remote districts in Sabah include misreporting of the place of occurrence as the usual residence, delayed reporting, non-coverage, ignorance of the law, inaccessibility, presence of a large number of migrants, miscommunication, and errors in data entry. The under-reporting of births may have serious consequences, such as misallocation of resources and deprivation of services to those affected. In line with the transformative promise of “leaving no one behind,” the Sustainable Development Goals urge all countries to strive to improve data quality for planning; this includes complete birth registration for creating effective development programs to reach target groups more effectively.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".