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Images of associative psychology as a generator of architectural education ideas

2020· article· en· W3049335693 on OpenAlexaff
Smolova Marina, Smolova Daria

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsMcGill University
Fundersnot available
KeywordsMemorizationConsciousnessArchitectureProcess (computing)Associative propertyProsperityReflection (computer programming)Cognitive scienceThinking processesKey (lock)PsychologyComputer scienceEpistemologyCognitive psychologyMathematics educationPolitical scienceVisual artsMathematicsArtPhilosophy

Abstract

fetched live from OpenAlex

Abstract Over the millennia, the human mind underwent numerous alterations, each time accumulating more and more information in itself. In human consciousness, associations are one of the key elements in the development of abstract intelligence and thinking. The process of memorization involves the creation of associations that can be found in any process of human activity, especially in architecture, art and design. Associations assist in rational evaluation of architecture and design as a source of new ideas and images. They become a fabric of innovative methods in discovering human-nature connectiveness, which is being expressed on our urban environment, communities and people. The significance of associations in architectural education derives from students’ participation in the formation of urban fabric and thus it is central to analyze associative mechanisms and typologies to evaluate trends and prosperity of cities. The paper will include stages of associative thinking, types and their reflection on students’ works.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.008
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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 designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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