The Matthew Effect: Infant Mortality in Canada and Internationally
Bibliographic record
Abstract
OBJECTIVE: To examine whether the magnitude of improvement in the health status of a population over time is dependent on the previous health status of that population. DESIGN AND SETTING: A study of infant mortality rates in Canada's 12 provinces and territories between the periods 1961-1965 and 1991-1995, and of infant mortality rates in 133 countries between 1960 and 1995. MAIN OUTCOME MEASURES: Spearman's rank correlations, relative risks, and risk differences to measure the relationship between infant mortality in the 1960s and changes in infant mortality between the 1960s and 1990s. RESULTS: In Canada, regional rankings based on infant mortality rates in 1961-1965 were strongly correlated (inversely) with rankings based on the percent change in infant mortality between 1961-1965 and 1991-1995 (correlation coefficient = -.85). In contrast, internationally, rankings based on infant mortality rates in 133 countries in 1960 were positively correlated with percent change between 1960 and 1995 (correlation coefficient =.56). Regional differences in infant mortality rates, measured using relative risks, declined in Canada (highest relative risk: 4.2, compared with Ontario in the 1960s; highest relative risk: 2.2, compared with Ontario in the 1990s) but increased globally (highest relative risk: 5.0, compared with industrialized countries in 1960; highest relative risk: 15.1, compared with industrialized countries in 1995). CONCLUSIONS: Canadian regions with higher infant mortality rates in 1961-1965 achieved larger improvements compared with regions with initially lower infant mortality rates. The pattern observed within Canada is unlike the pattern observed internationally.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".