Cherishing all the Children Equally? Ireland 100 Years on from the Easter Rising
Bibliographic record
Abstract
Cherishing All the Children Equally? 10.6 Average Difference in Weight Between the Children of Professional Parents and All Other Classes at Birth, Age 9 Months and Age 3 206 10.7 Proportion of Children Breastfed and Proportion Weaned onto Solid Foods after Age 5 Months, by Social Class 208 10.8 'Drumcondra' Reading and Maths Test Scores by Child Chronic Illness or Disability with Extent of the Mean Emotional and Behavioural Difficulties 210 11.1 Healthcare Entitlement Groups, by Cohort and Age 220 11.2 Average Annual Household Income, by Entitlement Group, Cohort and Age 221 11.3 Proportion of Mothers with a Third Level Qualification, by Entitlement Group, Cohort and Age 222 11.4 Proportion of 'Very Healthy' Children, by Entitlement Group, Cohort and Age 222 11.5 Average Number of GP Visits per annum, by Cohort and Age 224 11.6 Average Number of GP Visits per annum, by Entitlement Group, Cohort and Age 225 11.7 Average Number of GP Visits per annum, by Child Health Status, Cohort and Age 226 11.8 Average Number of GP Visits per annum, by Mother's Education, Cohort and Age 227 11.9 Average Number of GP Visits per annum, by Mother's Smoking Behaviour during Pregnancy, by Cohort and Age 227 11.10 Number of Additional GP Visits per annum, by Entitlement Group, Cohort and Age 229 11.11 Average Number of GP Visits per annum, by Equivalised Family Income, Cohort and Age (Private Patients)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".