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Record W2990698691 · doi:10.22215/etd/2016-11590

Building a Community: A Vernacular Strategy for Mixed Income Residential Housing in St. John's Newfoundland

2016· dissertation· en· W2990698691 on OpenAlexaboutno aff
Renaude Laberge-Boisjoli

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownVernacularAccommodationSustainable livingAffordable housingGeographyCommunity designSustainable communityPopulationArchitectural engineeringEconomic growthEnvironmental planningSustainabilitySociologyEngineeringPolitical scienceEconomicsArchaeologyPsychologyEcology

Abstract

fetched live from OpenAlex

Through architectural design, this research project explores a vernacular strategy for mixed income, medium-density residential housing as a new place of community and dwelling. This thesis will investigate the urban condition of the existing residential typologies, as well as the potential for the addition of people to a downtown core which has experienced a steady population decline over the past 50 years. Focusing on downtown St. John’s, Newfoundland, this thesis considers options to design innovative living spaces that foster a more affordable and sustainable community. Finally, this thesis assesses options to adapt to existing communities responding to the range of income of the residents, while also allowing for an increase in density and a better accommodation of an increasingly diverse population.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.005
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.037
GPT teacher head0.371
Teacher spread0.334 · 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
Published2016
Admission routes1
Has abstractyes

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