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Record W2913984005

Behind breakdown: the case of the MV Veteran

2018· dissertation· en· W2913984005 on OpenAlexaboutno aff
Donny Harry Persaud

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsSituatedNorm (philosophy)Function (biology)EngineeringBusinessEnvironmental planningPublic relationsGeographyPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Infrastructure as a topic of research has seen much attention within geographic and Science and Technology Studies literature. Frequently, studies of infrastructure explored the implementation of new infrastructures or the effects of infrastructural breakdowns on the relations sustained by these systems carrying the implicit assumption that infrastructures function seamlessly and disruptions constitute aberrations from a working norm. Recent works have problematized this assumption exploring the everyday practices of infrastructure’s users and the practices of repair and maintenance which sustain infrastructural systems. This thesis examines breakdown, repair, and maintenance of a new passenger ferry operating in a rural island community off Newfoundland’s north coast. This thesis extends current observations on emerging repair and maintenance literature from the situated activities of maintainers to the decision-making processes governing repair and maintenance. In doing so, this thesis illustrates how space, commonly held as a neutral surface on which these practices are undertaken, comes to influence how the passenger ferry is repaired and maintained as well as used by the islands’ residents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.249

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 studies0.0180.004
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.001

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 head0.017
GPT teacher head0.241
Teacher spread0.225 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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Same venueMemorial University Research Repository (Memorial University)Same topicCoastal and Marine ManagementFrench-language works237,207