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Record W4205709160 · doi:10.1007/978-3-030-84514-8_1

Introduction

2022· book-chapter· en· W4205709160 on OpenAlexaff
Lauren J. Wallace, Margaret MacDonald, Katerini T. Storeng

Bibliographic record

VenueGlobal maternal and child health · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsPolitical scienceAgency (philosophy)Equity (law)EthnographyPoliticsPublic policyHealth policyPublic administrationSociologyPublic relationsSocial scienceHealth careLaw

Abstract

fetched live from OpenAlex

Abstract This edited volume treats policy as an ethnographic object. Examining both policy spaces and sites of practice, the chapters illuminate both professionals’ and lay people’s intimate encounters with health policies. By ‘studying up’ and considering the multiplicity of actors and interests involved in global policies for improving maternal and reproductive health, the ten chapters in this volume track the processes and politics of policymaking and the mechanisms of their implementation in diverse contexts in Asia, Africa, Europe and South America. The chapters provide in-depth analyses of the complexities of policy formulation and implementation, the impact of socio-political contexts, as well as issues of local agency, equity and accessibility. Together, they demonstrate the value of ethnography as well as reproduction as a unique site for the generation of rich insights into the working of global health policies and their impacts. Such critical social science research is increasingly recognised as a crucial part of the evidentiary basis upon which people-centred and equitable health policy and systems everywhere are built. This volume will be of interest to scholars working at the intersection of critical global health, medical anthropology, and health policy and systems research, as well as to global public health practitioners.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.255
Teacher spread0.247 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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