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Record W2994695921 · doi:10.37113/ideaj.vi0.261

Interior architecture in Australia and Canada

2019· article· en· W2994695921 on OpenAlexaboutno aff
Marina Lommerse, Nancy Spanbroek

Bibliographic record

VenueIDEA JOURNAL · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthArchitectureInterior designWork (physics)PopulationInterior architectureEconomic growthEngineeringSociologyPolitical scienceArchitectural engineeringGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

This paper is the first in a series. The series will compare development in Interior Design/Interior Architecture in two commonwealth countries- Australia and Canada. These countries are of a similar population, size, language, mother country and age in terms of western development. The countries therefore share, in terms of design development, some of the same opportunities and barriers.Little documented research exists concerning the recent developments in Interior Architecture in Australia and Canada. Thus this paper was written to provide an anecdotal overview of the profession in the two countries over the period described. It is acknowledged that this is by no means an exhaustive piece of research of this period, but rather an overview and starting point for more in- depth research. This paper is the second in a series of comparative studies between education and practice in Interior Design/Interior Architecture between Canada and Australia. This paper discusses the similarities in the educational structure at universities within Australia and between Australia and Canada causing concern as we find ourselves in a global competitive market place. It is paramount that interior design education directs industry, and not be dictated by short-term industry demands. For this to occur, national educational bodies need to examine their existing programs in respect to one another, develop a distinctive approach in what they teach, and develop better communications with industry in order to ensure the sharing of valuable knowledge gained through project work. This paper is written to provide an anecdotal overview of the professional education in these two countries over the past twenty years.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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