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Record W3086259847 · doi:10.22215/etd/2017-11882

Ruin-Ophilia: Preserving Cultural Narratives of a Lighthouse Through Controlled Ruination

2017· dissertation· en· W3086259847 on OpenAlexaff
Zeynep Ekim

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsCarleton University
Fundersnot available
KeywordsNarrativeGeorgianPeninsulaHistorySAFERCultural transmission in animalsAestheticsEngineeringArtArchaeologyComputer scienceLiterature

Abstract

fetched live from OpenAlex

Once vital aspects of safer navigation routes and icons of industrial development, the Imperial Towers of Lake Huron and the Georgian Bay dominated over the Bruce Peninsula coastal landscape for almost two centuries.Their contribution to the development of their respective regions rendered them cultural landmarks and embedded them in the larger cultural narratives of their locales.However, advancements in technologies, like many other engineering works, led these structures to become obsolete.Among these is the Nottawasaga Island Lighthouse, now with all alternative use options exhausted, awaiting its end.This thesis explores a way to turn this ruination process into an architectural experience.Through "controlled ruination" the transmission of larger cultural narratives is enabled while the manmade melts into nature.ii AckNOwLedgemeNts I would like to thank my advisors Mariana Esponda and Susan Ross for all their support and encouragement.Without their extensive knowledge and great advice, this thesis would not have been accomplished.Thank you for giving me the passion to work with historic structures.I am

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.013
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.119
GPT teacher head0.309
Teacher spread0.190 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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