Commenting on an international perspective on the undercount of young children in the U.S. Census (DOI: 10.3233/SJI-161008)
Bibliographic record
Abstract
There appears to be something special about young children when taking a census! Dr. O'Hare's paper clearly demonstrates that young children are undercounted in the censuses of numerous countries, all of them conducting traditional censuses in one form or another. And for some of them he shows further that this undercount has been the case for decades. More specifically a net undercount for children under age 5 is common and it is typically higher than the net undercount for older children. These facts have seemed surprising and are certainly not well understood by the profession. He states "I am not aware of any published theories that attempt to explain the strong association between age and net undercount rates for children." Regarding the USA. he also poses the question ". . . why has there been consistently high net undercount rates for young children since 1950 while the net undercount rate for adults steadily improved?" I agree that more research in these areas is needed.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.032 | 0.048 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".