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Record W2596461184

Photovoice and Documenting Change in the Canadian North: Expanding Opportunities and Addressing Changes

2015· article· en· W2596461184 on OpenAlexvenueaboutno aff
Raynald Harvey Lemelin, Elaine Wiersma, Kirsten Baccar, Randy Kapashesit, Lillian Trapper, Michel S. Beaulieu, Donna Ashamock

Bibliographic record

VenueCanadian journal of native studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoicePhoto elicitationCitizen journalismMayaVisual artsRepresentation (politics)SociologyMedia studiesHistoryAnthropologyArtPolitical scienceComputer scienceWorld Wide WebArchaeologyLaw
DOInot available

Abstract

fetched live from OpenAlex

IntroductionSatellites images, computer models, photographs and videos depicting climatic transformations in the Canadian North have been used extensively. Yet, apart from the book by Abbott (2010), most of these images (climatic or otherwise), largely produced by experts and/or researchers, often devoid of local voices and/or local perspectives. In most cases, images of change often used to confirm the postulations of researchers (Prosser 1998). Thus, the issues of representation and trustworthiness of these images can become highly contested. The exclusion of local voices in such is somewhat surprising when one considers that the incorporation of photographs, film, and other visual creations in the social sciences is well established (Geller 2004; Pink 2001; Rose 2011; Schwartz 1989). Participatory visual methods like photo and video elicitation, blogs, vlogging (video blogging), and digital hypermedia are now part of visual vocabulary signposting a future direction of communicating visual research (Prosser 2012: 480). Indeed, collaborative and visual tools increasingly being used to develop rich and deep understandings of individuals and groups and their beliefs, cultures, traditions, and social relations (Lykes in collaboration with the Association of Maya Ixil women-New Dawn, Chajul, Guatemala 2001); giving participants a voice (Wang, Morrel-Samuels, Hutchison, Bell and Pestronk 2004); providing an additional qualitative approach for enriching and complementing data from other sources (e.g., semi-structured interviews, quantitative surveys) (Markwell 2000); and sharing the stories of pictures between group or community members, researchers and other invited participants (Wang and Pies 2004; Wang et al. 2004).One of the most popular and common methods in participatory visual is Photovoice. Building on participatory action approaches (Louis 2007; Heron and Reason 2001; Reason and Bradbury 2006), Photovoice involves participants as co-researchers throughout the process from data collection (i.e., photographs) to dissemination and is used in a multitude of contexts (e.g., climage change, education, food security, public health, education, public health) (Baldwin and Chandler, 2010; Lardeau et al. 2011; Wang and Burris 1997; Wang et al. 2004). Of particular interest to this discussion is the 2008 Photovoice study, depicting environmental health and social well-being, conducted by Castledon, Garvin and the Huu-ay-aht First Nation in British Columbia, Canada. Five fundamental themes emerged demonstrating the method's success in: balancing power between the First Nations researchers and academic researchers, creating a sense of ownership in the research, fostering trust, building capacity, and implementing a culturally appropriate project in the community contributing to the community's collective knowledge. These findings similar to those observed by Wang and Burris (1997) who noted that the goals of Photovoice threefold : (1 ) to enable people to record and reflect their community's strengths and concerns, (2) to promote critical dialogue and knowledge about important community issues through large and small group discussion of photographs, and (3) to reach policymakers (Wang and Burris 1997 p. 370).Few articles with the exception of Wang and Burris (1997) and the overviews of Photovoice applications in various settings by Hergenrather et al. (2009), Catalani and Minkler (2010) and Lai et al. (2012) discuss the general guidelines attributed to Photovoice. However, the protocol available is usually delineated in the following manner. Participants first given cameras and asked to photograph situations best depicting the topic of research. Participants then asked to select pictures they consider to be most significant and contextualize them by assigning captions and describing the stories associated with the picture (Wang and Burris, 1997). …

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How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.346
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.941
GPT teacher head0.667
Teacher spread0.274 · 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

Labeled directly by 2 models reading the full record.

Science and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

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

Citations4
Published2015
Admission routes2
Has abstractyes

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