On Becoming a Mathematical Demographer—And the Career in Problem-Focused Inquiry that Followed
Bibliographic record
Abstract
I greatly appreciate this opportunity to reflect on my career. Looking back over five decades of involvement in demographic and sociological scholarship, I have tried to say a bit about my personal life and my work—from developing mathematical models of fertility early on, to applying lessons from those models to empirical work in the United States, Bangladesh, and elsewhere in the developing world, to involvement in evaluations of health and population interventions. Equally important to me have been the building of research capacity and involvement in program and policy development. So much remains for new generations of scholars to do, but my hope is that, in choosing their own directions, they—and sociology as a whole—will take as their mission examining issues of societal importance around the world.
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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 | 0.038 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".