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Record W824602167 · doi:10.57709/2383719

Sickness, Violence and Reconciliation: Congenital Heart Disease in Iraq

2023· article· en· W824602167 on OpenAlexfundno aff
Deborah Phillips

Bibliographic record

VenueDigital Archive @ GSU · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
FundersUniversity of OxfordCanadian Medical AssociationHarvard University
KeywordsHeart diseaseDiseaseMedicinePsychiatryCriminologyMedical emergencyPolitical sciencePsychologyCardiology

Abstract

fetched live from OpenAlex

Congenital heart disease affects tens of thousands of children and families throughout Iraq, where complex surgical treatment remains largely unavailable. Through participant-observation and in-depth interviews, I investigated the understandings of this disorder among families in two areas: Kurdish northern Iraq and Arab southern Iraq. I pay particular attention to families perspectives on causes and treatment of the disorder in relation to historical and current macrosocial forces. Among the families I spoke with, there is a strong connection between the recent history of violence in Iraq and congenital heart disease. This thesis is largely an attempt to understand the uses and implications of this connection between sickness and violence for Iraqi families pursuing treatment through an international non-governmental organization.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.012
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.377
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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