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Record W4389054886 · doi:10.3389/fsufs.2023.1176101

Studying a gender responsive vaccine system: retrospective analysis of best methods

2023· article· en· W4389054886 on OpenAlexfundno aff
Sarah McKune, Alessandra Galié, Berkeley Miller, Salome A. Bukachi, Winnie Bikaako, Rhiannon Pyburn

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersConsortium of International Agricultural Research CentersInternational Development Research CentreUniversity of RwandaBill and Melinda Gates Foundation
KeywordsEmpowermentWork (physics)Qualitative researchValue (mathematics)LivestockKnowledge managementSociologyPublic relationsBusinessPolitical scienceEconomic growthGeographyEngineeringSocial scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

This methodological paper introduces four projects, all of which aimed to increase women’s engagement in and benefit from the livestock vaccine value chains of small ruminants and poultry by improving women’s empowerment and supporting women’s access to animal health services. All four projects used a mix of qualitative and quantitative research methods to understand the livestock vaccine system. Despite these shared aims, selected value chains, and research methods, the projects took different approaches to understanding the technical barriers for women’s engagement and benefit, women’s empowerment in the areas where they work, the policy landscape and implications, and gender norms of the societies where they work. The goal of this paper is to introduce the four projects, describe each project’s distinct research approach, and compare across projects how various qualitative and quantitative research methods contributed to understanding four elements which we identified as necessary for a fully functioning, gender responsive vaccine system: technical aspects (acumen/flow/effectiveness), women’s empowerment, policy environment, and gender norms.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.305
Teacher spread0.254 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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