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Record W4233289588 · doi:10.22215/etd/2021-14564

Learning to Listen to Place: Beyond Restoration at the Former Royal Alberta Museum

2021· dissertation· en· W4233289588 on OpenAlexaboutno aff
Nastassia Usenka

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)Adaptive reuseSustainabilityArchitectureActive listeningReuseIndigenousEnvironmental ethicsArchitectural engineeringEnvironmental resource managementPolitical scienceEngineeringSociologyGeographyEcologyCivil engineeringArchaeologyEnvironmental science

Abstract

fetched live from OpenAlex

In light of the climate crises of the 21st century, the need for the conservation, restoration and adaptive reuse of existing buildings is crucial to meeting Canadian and global ecological sustainability goals. Focusing on the adaptive reuse of the building and site of the former Royal Alberta Museum in Edmonton, Alberta, this project promotes a holistic approach to sustainability that equally recognizes societal, ecological and economic considerations. This thesis argues that a material and conceptual methodology of critical questioning, disassembly, evaluation, and reassembly can create a culture of renewal and repair in which architecture consistently adapts to our future needs. By listening to Indigenous ways of knowing and by framing time through the seven generations model, the thesis places itself in opposition to current, fast-paced building methodology. It suggests a future that moves towards conciliation through a multi-generational process to create new, heartfelt and lasting connections to people and place.

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)0.007
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.529
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0300.036
Scholarly communication0.0160.006
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0150.002

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 head0.022
GPT teacher head0.259
Teacher spread0.236 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same topicConservation Techniques and StudiesFrench-language works237,207