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Record W2593558720 · doi:10.26522/ssj.v10i2.1428

spareparts.exchange: Rahim and Robert, Stitched Together in Silence (Creative Intervention)

2016· article· en· W2593558720 on OpenAlexafffundvenueabout
Monir Moniruzzaman, Camille Turner, Heather Dewey-Hagborg, J. Ruxton

Bibliographic record

VenueStudies in Social Justice · 2016
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsYork University
FundersBrock University
KeywordsCommodificationSpare partSilenceSociologyCurrencyBusinessLawAestheticsEconomicsEconomyPolitical scienceMarketingArt

Abstract

fetched live from OpenAlex

Spare Parts (spareparts.exchange) is an art installation created collaboratively by Heather Dewey-Hagborg, Jim Ruxton, Camille Turner, and Monir Moniruzzaman. Based on Monir Moniruzzaman’s ethnographic research on the illicit organ trade, Spare Parts explores the ethics of organ trafficking and the emergence of bodily inequality in times of transplant tinkering. In this installation, the viewer is confronted with life sized video projections of Rahim Sheikh, a Bangladeshi kidney seller and Robert Zurrer, a Canadian kidney transplant recipient/buyer, whose kidneys were commodified in the marketplace. The video projections are installed so that the individuals sit in silence facing each other. Spare Parts highlights the intimacy of spare parts, the economy of the global marketplace, the perils of techno-medicine, and what is means to be human in the 21st century. The installation captures that the buying and selling of body parts is not just a market transaction, but rather represents the desperation, dis/connection, and inequality that exists in the trade.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0220.006

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 head0.049
GPT teacher head0.386
Teacher spread0.338 · 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
Published2016
Admission routes4
Has abstractyes

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