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Record W2996081474 · doi:10.4000/tc.12372

Viscous Objects. The Uneven Resistances of Repair

2022· article· en· W2996081474 on OpenAlexaboutno aff
Donny Persaud, Josh Lepawsky, Max Liboiron

Bibliographic record

VenueTechniques & culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsWarrantyObject (grammar)Computer scienceOperations researchBusinessEngineeringPolitical scienceLawArtificial intelligence

Abstract

fetched live from OpenAlex

The MV Veteran, a seagoing car ferry constructed by the Dutch industrial conglomerate Damen Shipyards Group, connects the two small, rural outport communities of Fogo Island and Change Islands to the larger island of Newfoundland, Canada. Our paper examines the breakdowns of this new ferry and the repairs facilitated through its manufacturer’s warranty. In our analysis, we treat the warranty as a ‘script’ as something that both constrains and enables action and engenders resistances. The warranty anticipates and conditions both future times and spacings of breakdown and how those future breakdowns are to be addressed through activities such as maintenance and repair. In recounting the ferry’s breakdowns and repairs, we explore how the case of this ferry and its warranty in this particular place suggests a need to add to the analytical and conceptual repertoire for thinking about the breakdown and repair of technical objects. We suggest there is a need for supple enough concepts of breakdown and repair that they can deal with technical objects that are neither as ‘fluid’ as the classic example of a bush-pump nor quite as ‘fixed’ as infrastructures embedded in landscapes such as electrical grids or canals. Thus, we think with the notion of the ‘viscosity’ of technical objects. We claim that the importance of the term viscosity in the sociotechnical lexicon is not to demark a halfway point between fluid and fixed, mobile and immobile, but to point out how a single object like a ferry can be all of these things, unevenly, at once through characterizing its multiple resistances following breakdowns.

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)0.010
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.036
Scholarly communication0.0090.016
Open science0.0010.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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
Published2022
Admission routes1
Has abstractyes

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