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Record W7033700133

Robin Boyd: The Wizard of Oz

2019· article· en· W7033700133 on OpenAlexaboutno aff

Bibliographic record

VenueRMIT Research Repository (RMIT University Library) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicTardigrade Biology and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPolymathNewspaperNarrativeWizard of ozNexus (standard)Digital media
DOInot available

Abstract

fetched live from OpenAlex

Robin Boyd was a polymath whose creative work reverberated across a range of platforms from interiors, architecture, garden design, writing, illustration and public broadcasting. Given this, it is easy to see Boyd as a cultural producer who could create outputs in different media channels. However underlying this media polymath was a bigger agenda: one driven by a desire to explore how spaces were experienced along with communication a broad audience. While traditional forms of media such as Boyd’s well-known television broadcasts, newspaper columns and best-selling books were one way to achieve this, lesser known is Boyd’s explorations into the nexus between architectural space, sound theatre and the new media technologies of the 1960s. Boyd’s theoretical flair with a range of multimedia design experiments are yet to receive the analysis they deserve, yet this analysis is critical in contributing to an alternate view of established Anglo-centric accounts of Australia’s history. At Expo ’67 in Montreal, Canada, Boyd’s ‘Sound Chairs’ embedded pre-recorded tape recordings to create a sonic narrative of Australian identity. At Expo ’70 in Osaka, Japan, Boyd’s ‘Space Tube’ design combined a range of media and spatial apparatuses to create an immersive experience of Australian life. In examining Boyd’s Expo ’67 and Expo ’70 designs, it will be ascertained how and to what degree Boyd sought to evoke altered states.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.204
Teacher spread0.183 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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