Participatory Visual and Digital Methods (2013) by Aline Gubrium & Krista Harper
Bibliographic record
Abstract
This book explores of participatory visual and digital methods (PVDM), which may also be understood as Participatory Action Research (PAR).These methods use different forms of multimedia.Posited as a how to do PVDM for undergraduate and graduate students and university faculty, the book is intended for veteran as well as novice researchers.The authors provide examples of four different kinds of participatory and visual methods in addition to theoretical and ethical considerations for using these research designs.The book is divided into 10 chapters and may be broken up into three sections: why researchers should consider using PVDM (Chapters 1 and 2), different ways to use it (Chapters 4, 5, 6, and 7), how to archive and exhibit it (Chapter 8), and ethical and analytical considerations researchers must consider while obtaining data and turning their research into a final product (Chapters 3, 9, and 10).PVDM may be particularly interesting for investigators who use PAR in their studies, conduct research in a community where they are already familiar, or desire a tangible final product other than a scholarly paper.In the introductory chapter, Gubrium and Harper establish what PVDM research methods are and provide the general outline of the book.In Chapter 2, the authors examine the history of PVDM, consider critiques of ethnography, which is a method used frequently in anthropology to gather data on particular cultural groups and propose several theoretical perspectives.Chapter 3 reviews the ethical implications of conducting PVDM and explains how the approach allows research subjects to participate in the research process by creating multimedia projects based on their lives.This chapter also discusses how power imbalances, reciprocity, and representation should be considered for the benefit of the research participants.Chapters 4 through 8 provide case studies about particular kinds of PVDM approaches, which are PhotoVoice, participatory film and videomaking, digital storytelling, participatory Geographic Information Systems, and participatory digital archives and exhibitions.Chapter 9 focuses on the importance of reflexivity in ethnographic research and how this can be connected with PAR.The final chapter poses questions for further thought, yet Gubrium and Harper admit they "end the book perhaps with more questions than answers given" (p.25).The authors believe PVDM and PAR methodologies have decolonizing properties and they see the potential for the Indigenous populations that have historically been over-researched (Smith, 2012) to be able to actively engage in the research process.In Chapter 2, Participatory Visual and Digital Research in Theory and Practice, Gubrium and Harper refer to Tuhawai Smith's (2012) often cited book Decolonizing Methodologies, now in its second edition, and the importance of decolonizing the research process.Contrary to what the authors intend, the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".