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Record W2100145943 · doi:10.1177/2158244014530997

Turning the Camera Back

2014· article· en· W2100145943 on OpenAlexaff
Bonnie Fournier, Andrea Bridge, Judy Mill, Arif Alibhai, Andrea Kennedy, Joseph Konde-Lule

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMount Royal UniversityUniversity of Alberta
Fundersnot available
KeywordsPhotovoiceParticipatory action researchCitizen journalismSociologyPhoto elicitationVisual researchExploratory researchQualitative researchAction (physics)Public relationsEngineering ethicsPsychologyPolitical scienceSocial scienceAnthropologyEngineeringEconomic growthVisual arts

Abstract

fetched live from OpenAlex

There is limited literature describing the methodological and pragmatic considerations that arise when conducting participatory action research utilizing Photovoice with children, particularly within sub-Saharan Africa. We provide a case example of these considerations based on a qualitative exploratory design that was conducted in June 2010 with 13 children between the ages of 12 and 18 years who were orphaned and living with HIV in a group home setting in Western Uganda. The main purpose of this study was to explore the children’s experiences while including them in a participatory way utilizing Photovoice to share their stories, define their issues, and propose their own solutions. Conducting research in another country where language and culture are different from the researchers’ can pose many unique methodological, epistemological, and ethical challenges. These issues are discussed by reflecting on the process of the study. Key lessons will also be discussed regarding the methodological and pragmatic considerations with the aim of providing new insights for researchers who want to conduct research in a cross-cultural and multilingual setting.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0970.024

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 head0.614
GPT teacher head0.658
Teacher spread0.044 · 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.

Study designQualitative
DomainMethods
GenreMethods

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

Citations7
Published2014
Admission routes1
Has abstractyes

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