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Record W2924273135 · doi:10.22215/etd/2017-11835

The Architecture of Emotional Drawings

2017· dissertation· en· W2924273135 on OpenAlexaboutno aff
Jeniffer Milburn

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessArchitectureUnconscious mindAffect (linguistics)Context (archaeology)Scope (computer science)Expression (computer science)PsychologyAestheticsQualitative researchSocial psychologyVisual artsSociologyArtComputer scienceCommunicationPsychoanalysisSocial scienceHistory

Abstract

fetched live from OpenAlex

Architecture is a story which unfolds through drawing.The experience of architecture aff ects us, it stimulates people and can aff ect their wellbeing.Thus, happiness is the foremost signifi cant emotion in the architecture of emotional drawings.Drawing can reveal what the spoken word cannot, it can bequeath wellbeing as it is an expression of conscious and unconscious desires.Drawings are essentially constructed of subjective artistic experiences; whilst healthcare is grounded in objective scientifi c research.To explore the context of drawing and wellbeing at the Children's Hospital of Eastern Ontario CHEO, it is necessary to consider both frameworks.It is the scope of this thesis to research a qualitative phenomenological approach, revealed through drawings, to express how one experiences happiness and by connecting it to existing evidencebased and quantitative research.How can drawing become a way of imagining the new addition at CHEO; a cultivated place which can stimulate an environment of happiness and wellbeing.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.021
Scholarly communication0.0110.006
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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