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Record W2344735436 · doi:10.1080/17441692.2016.1165720

Beyond engagement in working with children in eight Nairobi slums to address safety, security, and housing: Digital tools for policy and community dialogue

2016· article· en· W2344735436 on OpenAlexaff
Claudia Mitchell, Fatuma N. Chege, Lucy Maina, Margot Rothman

Bibliographic record

VenueGlobal Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsCitizen journalismPublic relationsParticipatory action researchSociologyRelation (database)SlumPolitical scienceComputer sciencePopulation

Abstract

fetched live from OpenAlex

This article studies the ways in which researchers working in the area of health and social research and using participatory visual methods might extend the reach of participant-generated creations such as photos and drawings to engage community leaders and policy-makers. Framed as going 'beyond engagement', the article explores the idea of the production of researcher-led digital dialogue tools, focusing on one example, based on a series of visual arts-based workshops with children from eight slums in Nairobi addressing issues of safety, security, and well-being in relation to housing. The authors conclude that there is a need for researchers to embark upon the use of visual tools to expand the life and use of visual productions, and in particular to ensure meaningful participation of communities in social change.

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)0.009
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.019
Scholarly communication0.0070.005
Open science0.0020.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.439
GPT teacher head0.545
Teacher spread0.105 · 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 designQualitative
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

Citations15
Published2016
Admission routes1
Has abstractyes

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