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MATERIAL WITNESS

2020· book· en· W4249633456 on OpenAlexfundaboutno aff
Susan Schuppli

Bibliographic record

VenueThe MIT Press eBooks · 2020
Typebook
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersUniversity of CalgaryUniversity of Minnesota
KeywordsWitnessSensibilityMeaning (existential)HistoryAccident (philosophy)LawPolitical sciencePsychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

The evidential role of matter—when media records trace evidence of violence—explored through a series of cases drawn from Kosovo, Japan, Vietnam, and elsewhere. In this book, Susan Schuppli introduces a new operative concept: material witness, an exploration of the evidential role of matter as both registering external events and exposing the practices and procedures that enable matter to bear witness. Organized in the format of a trial, Material Witness moves through a series of cases that provide insight into the ways in which materials become contested agents of dispute around which stake holders gather. These cases include an extraordinary videotape documenting the massacre at Izbica, Kosovo, used as war crimes evidence against Slobodan Milošević; the telephonic transmission of an iconic photograph of a South Vietnamese girl fleeing an accidental napalm attack; radioactive contamination discovered in Canada's coastal waters five years after the accident at Fukushima Daiichi; and the ecological media or “disaster film” produced by the Deep Water Horizon oil spill in the Gulf of Mexico. Each highlights the degree to which a rearrangement of matter exposes the contingency of witnessing, raising questions about what can be known in relationship to that which is seen or sensed, about who or what is able to bestow meaning onto things, and about whose stories will be heeded or dismissed. An artist-researcher, Schuppli offers an analysis that merges her creative sensibility with a forensic imagination rich in technical detail. Her goal is to relink the material world and its affordances with the aesthetic, the juridical, and the political.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.008
Scholarly communication0.0080.009
Open science0.0020.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0650.007

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 head0.051
GPT teacher head0.307
Teacher spread0.256 · 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 designTheoretical or conceptual
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

Citations108
Published2020
Admission routes2
Has abstractyes

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