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Record W7006692063

The voice of the rural small town: how architecture can inspire locally grounded growth in Parry Sound, Ontario

2023· dissertation· en· W7006692063 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodHyporeflexiaDiafiltrationTSG101Fusible alloy
DOInot available

Abstract

fetched live from OpenAlex

Many rural areas in Canada are financially dependent on tourism. There are negative effects of this dependency on the local working class as these small towns transition from tourism towards a community designed for an aging affluent population. Using Parry Sound as a case study, this thesis attempts to investigate how architecture could serve the neglected local working class by incentivizing investment in the local community rather than catering to seasonal tourism. Covid-19 also emphasized the gap in access to healthcare and lack of overall well-being, issues that the local community faced but the seasonal resident did not. This thesis found that a grounded design that amplified the voice of the local community would best aid their existing efforts to grow. Discussions with the local community organizations working in these areas provided the perspectives necessary to design an adequate program that would assist local grounded growth in Parry Sound. Through the lenses of housing, health, and food, architecture could support the existing network of community initiatives to achieve self-agency. A mixed-use design provides the necessary community spaces for these initiatives to foster a connection to people and landscape and achieve locally grounded growth.

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)0.006
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: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.012
Scholarly communication0.0080.003
Open science0.0030.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.009
GPT teacher head0.171
Teacher spread0.162 · 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
Published2023
Admission routes1
Has abstractyes

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