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Record W2575917410 · doi:10.1111/cag.12337

Going unscripted: A call to critically engage storytelling methods and methodologies in geography and the medical‐health sciences

2017· article· en· W2575917410 on OpenAlexaffvenue
Sarah de Leeuw, Margot W. Parkes, Vanessa Sloan Morgan, J. Christensen, Nicole Lindsay, Kendra Mitchell‐Foster, Julia Russell Jozkow

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2017
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsSimon Fraser UniversityUniversity of Northern British ColumbiaQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsThe artsStorytellingHumanitiesSociologyGeneral partnershipMedical humanitiesNarrativeVisual artsArtPolitical scienceMedicineLiteratureMedical education

Abstract

fetched live from OpenAlex

Geography and the medical‐health sciences have long histories of engaging the humanities. The last decade has seen for both disciplines a significant growth in theoretical frameworks, pedagogic strategies, and research methods that draw upon visual and literary arts, critical self‐reflection, creative tools and expressions, and even direct engagement or partnership with artists, curators, authors, theatre‐practitioners, and other professionals in the arts. Both geographers and medical‐health professionals, then, are increasingly (re)making and understanding various worlds through the humanities. In this paper we explore the histories of humanities in both geography and the medical‐health sciences, especially medicine: we argue the two disciplines have much to learn from each other's engagement and work with the humanities. Focusing on the increasing use of narrative and storytelling in both disciplines, we argue that deployment of humanities‐based frameworks and impulses must not be taken up without careful and critical analytical reflection. Finally, we ground our theoretical explorations with empirical examples from recent community‐based work about the risks and benefits of storytelling and visual arts when looking at the health geographies of Indigenous and settler peoples in Northern British Columbia. De manière impromptue : vers une démarche critique sur les méthodes de mise en récit et les méthodologies en géographie et en sciences médicales et de la santé L'intérêt pour les sciences humaines par la géographie et les sciences médicales et de la santé s'inscrit dans une longue tradition. Au cours de la dernière décennie, les deux disciplines ont connu une importante croissance de cadres théoriques, de stratégies pédagogiques et de méthodes de recherche qui font appel aux arts visuels et à la littérature, à l'autoréflexion critique, à des outils et modes d'expression novateurs, voire même à une participation directe ou à des partenariats avec des artistes, conservateurs, auteurs, praticiens de l'art dramatique et d'autres professionnels du domaine des arts. Autant les géographes que les professionnels de la médecine et de la santé contribuent de plus en plus à (re)constituer et comprendre divers mondes à travers les sciences humaines. Cet article brosse un tableau historique des sciences humaines tant en géographie qu'en sciences médicales et de la santé, en particulier la médecine : nous soutenons que les deux disciplines ont beaucoup à apprendre l'une de l'autre sur l'intérêt que chacune porte aux sciences humaines. En mettant l'accent sur le recours grandissant par les deux disciplines à la narration et à la mise en récit, nous faisons valoir l'idée que le déploiement des cadres et des impulsions fondés sur les sciences humaines ne peut pas être envisagé sans mener au préalable une réflexion analytique minutieuse et critique. Enfin, nous fondons cette étude du champ théorique sur des exemples empiriques tirés de travaux réalisés à l'échelle communautaire sur les risques et les avantages de la mise en récit et des arts visuels quand on se penche sur les aspects géographiques de la santé des peuples autochtones et colonisateurs dans le nord de la Colombie‐Britannique.

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 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)0.009
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0030.010
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.359
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations72
Published2017
Admission routes2
Has abstractyes

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