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VAN DUZER MEMORIAL ORATION: THE EFFECT OF ARMED CONFLICT ON CHILD HEALTH AND DEVELOPMENT

2010· article· en· W1537039636 on OpenAlexaff
Alvin Zipursky

Bibliographic record

VenueFamily Court Review · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPresentation (obstetrics)Government (linguistics)Armed conflictHealth carePolitical scienceCriminologyMedicineEconomic growthPsychologyLaw

Abstract

fetched live from OpenAlex

Armed conflict has occurred in many parts of the world for centuries and undoubtedly will occur in the future. In the past, combatants suffered; now the majority of suffering is by civilians. Children in these regions are denied the benefits of health care and normal nurturing both during and especially for prolonged periods after the conflict (when the health and social systems have been disrupted or are non‐existent). Their problems of health and development are major; problems for which the experience and knowledge of pediatrics and pediatric research could contribute. Yet, to date, the study of the health and development of children in war zones has not been a major priority of pediatric societies or of the large community of pediatric clinicians and researchers. Recently the Programme for Global Pediatric Research has held meetings with representatives of agencies working in areas of armed conflict together with pediatric clinicians and researchers. They explored the health and developmental problems of children in war zones. Recommendations from those meetings highlighted the plight of mothers and children during conflict and in the period “after the shooting stops.” Child health and development is critically affected during these times. In many instances planning has been inadequate and both government and legal support have been deficient. This presentation will describe the health and developmental problems of children in zones of armed conflict and steps to be taken to alleviate these major problems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.573
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.427
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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