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Record W2936937967 · doi:10.22215/etd/2018-12931

Our Barns: Our Stories

2018· dissertation· en· W2936937967 on OpenAlexaboutno aff
Darby Ace

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsnot available
Fundersnot available
KeywordsCraftAdaptive reuseBarnContext (archaeology)Architectural engineeringReuseIntangible cultural heritageIdentity (music)EngineeringAdaptation (eye)Cultural heritageCivil engineeringSociologyGeographyAestheticsArchaeologyArt

Abstract

fetched live from OpenAlex

This thesis will explore how the concept of intangible cultural heritage can inform the design of a tangible form in a changing cultural context.Specifically, it will question how the intangible concepts of community, craft, and story can inform a design strategy which acts to integrate the adaptive reuse of a traditional barn with its new suburban environment.The design strategy will prioritize flexibility both spatially and within the connection between old and new.Heritage buildings can act to inspire architects to think outside of the box and to raise the bar on suburban design.Timber framed barns are beautifully exposed examples of traditional craft and serve as a reminder of the agricultural history of an area.Historic barns provide a unique opportunity to give a new sense of identity to the evolving suburban culture.This project will explore the technical, conceptual, and contextual implications of adaptive reuse of a barn in a suburban environment.The Bradley-Craig Farm at 590 Hazeldean Road, Stittsville, Ontario will serve as a case study for this research and adaptation.

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)0.005
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 score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.016
Scholarly communication0.0090.010
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0230.004

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 head0.043
GPT teacher head0.279
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
Published2018
Admission routes1
Has abstractyes

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