A New Dataset on Infant Mortality Rates, 1816—2002
Bibliographic record
Abstract
Abstract Systematic data on annual infant mortality rates are of use to a variety of social science research programs in demography, economics, sociology, and political science. Infant mortality rates may be used both as a proxy measure for economic development, in lieu of energy consumption or GDP-per-capita measures, and as an indicator of the extent to which governments provide for the economic and social welfare of their citizens. Until recently, data were available for only a limited number of countries based on regional or country-level studies and time periods for years after 1950. Here, the authors introduce a new dataset reporting annual infant mortality rates for all states in the world, based on the Correlates of War state system list, between 1816 and 2002. They discuss past research programs using infant mortality rates in conflict studies and describe the dataset by exploring its geographic and temporal coverage. Next, they explain some of the limitations of the dataset as well as issues associated with the data themselves. Finally, they suggest some research areas that might benefit from the use of this dataset. This new dataset is the most comprehensive source on infant mortality rates currently available to social science researchers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".