Additional file 1 of Physical discipline as a normative childhood experience in Singapore
Bibliographic record
Abstract
Additional file 1: Table S1. Contingency Table for Frequency at Which Children Were Physically Punished by Their Mothers as Reported by Their Mothers and Fathers. Table S2. Contingency Table for Frequency at Which Children Were Physically Punished by Their Fathers as Reported by Their Fathers and Mothers. Table S3. Contingency Table for Frequency at Which Children Were Spanked by Their Mothers as Reported by Their Mothers and Fathers. Table S4. Contingency Table for Frequency at Which Children Were Spanked by Their Fathers as Reported by Their Fathers and Mothers. Table S5. Contingency Table for Frequency at Which Children Were Slapped by Their Mothers as Reported by Their Mothers and Fathers. Table S6. Contingency Table for Frequency at Which Children Were Slapped by Their Fathers as Reported by Their Fathers and Mothers. Table S7. Contingency Table for Frequency at Which Children Were Grabbed by Their Mothers as Reported by Their Mothers and Fathers. Table S8. Contingency Table for Frequency at Which Children Were Grabbed by Their Fathers as Reported by Their Fathers and Mothers. Table S9. Prevalence of Physical Discipline of Children by Their Parents at Age 4.5, 6, 9, and 11 According to Child Sex. Table S10. Prevalence of Physical Discipline of Children by Their Mothers and Fathers at Age 9. Table S11. Prevalence of Physical Discipline of Children by Their Mothers at Age 4.5, 6, 9, and 11 by Maternal Ethnicity.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.631 | 0.070 |
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".