Bibliographic record
Abstract
Sick and injured children, like combatants wounded by shot and shell in war, are disproportionately represented in the tallies of both man-made and national disasters. Paediatricians have a particularly proud heritage of military service, a nexus dating in Australia from the early 19th century. This paper traces this link between service to children in peacetime and the care of servicemen, women and children in times of war and disaster. The extraordinary record of Australian 'paediatric' doctors who also served in the Gallipoli Campaign (1915) is documented as an illustration of this duality. Paediatricians who serve in the Defence Reserves and in civilian non-government organisations which respond to disasters and civil wars have special credentials in their advocacy for the protection of children enmeshed in conflict or disaster. Such applies particularly to the banning of the recruitment and use of child soldiers; support for children caught up in refugee and illegal immigrant confrontations; and continued advocacy for greater international compliance with the Ottawa Convention to ban the use of anti-personnel landmines. Volunteering for such service must occur in cold 'down time', ensuring that paediatricians are trained in disaster and conflict response, when such challenges inevitably confront the paediatricians of the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".