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Record W3133291381 · doi:10.1177/1474474021993416

Travelling intimacies, translation and betrayal in a creative geography

2021· article· en· W3133291381 on OpenAlexaff
Caleb Johnston, Geraldine Pratt

Bibliographic record

VenueCultural Geographies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConversationDementiaOutsourcingSociologyVisual artsPublic relationsMedia studiesCare workWork (physics)Political scienceArtMedicineEngineeringLaw

Abstract

fetched live from OpenAlex

In 2019, we collaborated with German theatre artists to co-create Between Worlds: Outsourcing Dementia Care, an immersive, multi-media piece performed in Newcastle and Berlin. This performance work animated and staged our interviews conducted with the owners of and caregivers working in private care facilities recently built in northern Thailand to provide dementia care for overseas guests from across the Global North. This creation process also drew from interviews we conducted with the family members who had chosen this option for their loved ones with dementia. Incorporating elements of documentary theatre, movement and cinematic projection, Between Worlds was designed to bring audiences into an intimate space, drawing them close to the complexities of the outsourcing of dementia care in order to prompt public conversation and reflection on dementia care in both Thailand and the Global North. Here, we consider the performance of the play and the method that our theatre collaborators used to render transparent the process of translation within performance. We critically assess the outcome to question the possible betrayals implicit in creative and social science work and in the doing of cultural geography.

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)0.019
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0240.093
Scholarly communication0.0210.010
Open science0.0020.023
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.324
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

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