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Cancer on the Margins: Method and Meaning in Participatory Research

2010· article· en· W1489747706 on OpenAlexaboutno aff
Emily S. Kolker

Bibliographic record

VenueSociology of Health & Illness · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchSociologyCitizen journalismGeneral partnershipMeaning (existential)Community-based participatory researchPublic relationsPower (physics)Political sciencePsychologyLaw

Abstract

fetched live from OpenAlex

( eds ) Cancer on the Margins: Method and Meaning in Participatory Research . University of Toronto Press , 2009 $27.95 (pbk) xi+267 pp . ISBN 978-0-80209434-6 . In this ambitious anthology, Canadian researchers involved in a six-year, multi-project investigation of marginalised women’s experiences of breast cancer reflect on the core theoretical, methodological, and epistemological issues inherent to participatory research. In part a response to the abuses of past research that took advantage of marginalised groups, but also to the growth in critical theory on the social production of knowledge, participatory research has been put forth as an example of knowledge production in partnership with marginalised communities being studied. Above all, participatory research aims to share power in knowledge production by including marginalised communities in all phases of research including research design, data collection, data analysis, and the dissemination of research. Cancer on the Margins is organised according to these stages of research, and invites the reader to listen in on the ethical and practical questions raised in case studies of participatory research with marginalised women with breast cancer. The collection is intended for several audiences including undergraduate students in health-related courses, researchers interested in conducting participatory research, graduate students developing methodological skills, qualitative researchers interested in the ethical dilemmas raised in participatory research, health practitioners, patient and community advocates, and healthcare policy makers. In each substantive section, the book focuses on the ethical and methodological issues in participatory research, that is, the importance of identifying and reflecting on power differentials between researchers and the communities they study. The collection draws these issues to the surface for reflection on how power differentials shape each phase of the research process. The book admittedly offers no clear solutions to these dilemmas, nor does it offer a ‘how to’ recipe for conducting participatory research. What it does provide are glimpses into important moments in which researchers were confronted with the very power differentials they aim to eliminate through their research. The book is most illustrative of the dilemmas of power differentials in participatory research in the two sections on data analysis and representation. Each section uses specific examples of participatory research that walk the reader through either a challenging moment in their project, or how they engaged with these issues including the inclusion of community feedback, and the public representation of marginalised groups through the dissemination of findings. Qualitative researchers, including those who do not conduct participatory research, will recognise and appreciate these examples of the difficulties of data analysis and representation, issues that take on a unique meaning in psychosocial investigations of health and illness. These examples are useful not only for experienced qualitative health researchers, but also for graduate students who are typically hungry for specific examples of the range of ethical dilemmas they might face as a researcher in the field. And while the book explicitly offers no solutions to these dilemmas, there is great value in being able to examine other researchers’ struggles and choices throughout the research process. In this sense, the contributors are generous to expose and reflect on their own decision-making processes as researchers. The book succeeds in reaching a good portion of its intended audience. Portions of the book are written to familiarise readers with participatory research, including the importance and validity of qualitative research that takes the lived experience of health and illness as its starting point. Thus portions of the book read more as a justification for participatory research for healthcare audiences oriented towards a biomedical model of health and illness. Other sections, including researchers’ reflections on unequal ‘standpoints’ in participatory research, and the impact of knowledge production on marginalised communities, represent more intimate dialogues for those who already practise community-based research. Portions of the book are useful for different audiences and purposes, and therefore cannot be labelled as having only one or two audiences. Ironically, the book reveals another set of power differentials between qualitative researchers, namely the difference in material resources between researchers who conduct funded, participatory research and individual researchers who conduct research without outside funding sources, or without a team of people to provide feedback, checks on analysis, and general support. There is a certain amount of privilege that comes with participatory research that warrants future acknowledgment and dialogue. Lastly, one could argue that the attentiveness to the ethical dilemmas in participatory research in this anthology are the same for any qualitative research in the field of health and illness. It is because of this that Cancer from the Margins is an important reminder of the ethical and social dilemmas inherent in research on health and illness.

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)0.049
metaresearch head score (Gemma)0.029
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: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.029
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.007
Science and technology studies0.0070.030
Scholarly communication0.0140.012
Open science0.0030.010
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.807
GPT teacher head0.731
Teacher spread0.076 · 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
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".

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Citations3
Published2010
Admission routes1
Has abstractyes

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