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Record W4231013403 · doi:10.32920/ryerson.14653104.v1

Industrial Archaeology As Urban Informer: The Wellington Destructor

2021· preprint· en· W4231013403 on OpenAlexaff
Daniel Petrocelli

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUrbanityArtifact (error)Industrial heritageContext (archaeology)Identity (music)Urban planningIndustrial cityEnvironmental planningArchitectural engineeringArchaeologyCivil engineeringGeographyCultural heritageCultural heritage managementEngineeringIndustrial zoneAestheticsArtComputer science

Abstract

fetched live from OpenAlex

The city identity, city image and the recognition of its industrial past are at question in a quickly developing post-industrial urban context. The voices of industrial archaeology, of obsolete infrastructure, of unintended industrial monument in dialogue between fast developing new urban and past locus are all ingrained in the city’s memory. This urban discourse, if allowed to happen, will inform the development of contemporary urban fabric. It is vital that continuity of the built environment structures the contemporary post-industrial city identity This thesis engages with the Industrial artifact of the Wellington Destructor and suggests a conservation strategy for the obsolete and abundant industrial built artifact that will inspire new development and integrate within the masterplan. It will activate city’s past and future dialogue and it will inform the emerging urban development while preserving the continuity of urban heritage with industrial past. Industrial Archaeology becomes agent to changing urbanity.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.015
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.156
GPT teacher head0.249
Teacher spread0.093 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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