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Record W2944065845 · doi:10.22215/etd/2016-11344

More Than Material: The Vibrancy of the Car in the Volkswagen Diesel Scandal

2016· dissertation· en· W2944065845 on OpenAlexaff
Tyler Hale

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsCarleton University
FundersStrong
KeywordsMaterialismMarxist philosophyPoliticsHistorical materialismSociologyPolitical economyEnvironmental ethicsAestheticsEconomyPolitical scienceEpistemologyLawArtEconomicsPhilosophy

Abstract

fetched live from OpenAlex

This thesis pits the work of political theorist Jane Bennett, namely the theoretical paradigm of vibrant materialism, against conventional political economic approaches to material culture, and the wider political economy of automobile society.It asks, and answers, two overarching questions: what can Bennett's vibrant materialism contribute to more traditional Marxist style materialism, and what benefit can they, together, bring to discussions about material culture in anthropology?Secondly, how were the events of the Volkswagen diesel scandal of 2015, i.e. the unbridled success of clean diesel technology in the U.S., the duplicitous role of Volkswagen's 'defeat devices', and the entire network of characters involved, affected by the material composition of diesel cars and the wider political economy of automobility?I argue that Volkswagen's clean diesel cars are vibrant materials, which actively produce the subjectivities that are contrary to the ecological-sensitivity the technology is purported to advance.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.017
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.324
Teacher spread0.308 · 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
Domainnot available
GenreOther

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

Citations1
Published2016
Admission routes1
Has abstractyes

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