MétaCan
Menu
← Back to cohort
Record W4241233019 · doi:10.22215/etd/2021-14543

In Search of a Place for Emotion and Healing: Designing Empathetic Architecture for the Third Age

2021· dissertation· en· W4241233019 on OpenAlexaff
Joo Y. Hong

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchitectureSituatedSpace (punctuation)PopulationTypologyBuilt environmentDemographicsPrioritizationChoice architecturePsychologyAestheticsSociologySocial psychologyGeographyEngineeringComputer scienceManagement scienceArtificial intelligenceCivil engineeringDemography

Abstract

fetched live from OpenAlex

In medicalized environments, the link between emotion and architecture is often overlooked due to prioritization of efficiency and treatment. In such settings, the architecture no longer actively participates. The thesis investigates a typology of therapeutic architecture for the aging demographics in 21st century. Despite the rising number of population entering this generation, they have been neglected in terms of policy, investment, and accommodation. As such, the physical and emotional environment for this group has not been sufficiently addressed. The thesis departs by asking the question of how to create a healing space for the marginalized senior cohorts. Situated near Hospital Montfort, the created space will incorporate communal and sensory elements to generate a hospitable environment. At a broader level, the project will attempt to establish a symbiotic relationship with nature and surrounding long-term care housing to develop a holistic network for the aging 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)0.003
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.030
GPT teacher head0.304
Teacher spread0.275 · 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

Explore more

Same topicUrban Green Space and Health→French-language works237,207→