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Record W4411180832 · doi:10.62754/ais.v6i2.134

Editorial

2025· editorial· en· W4411180832 on OpenAlexaboutno aff
Nic Clear, Hyun Jun Park

Bibliographic record

VenueArchitecture Image Studies · 2025
Typeeditorial
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This is the third special issue that Clear+Park (C+P) have edited for AIS, and working with them has once again been a hugely enjoyable experience. In all our collaborations, we have enjoyed enormous freedom in the type of projects that can be included, especially the license to mix practice-based projects and speculative approaches alongside more traditional academic text-led pieces. In this edition, we look at the technologies of digital capture: 3D scanning, photogrammetry and depth, and motion detecting technologies. The introduction of these instrumental forms of capture has allowed highly accurate processes by which objects and spaces can be surveyed, mapped and digitised. Currently, this information is predominantly used to catalogue, record and document spaces and artefacts as part of a digital project workflow, often within a construction or heritage context. Author Biographies Nic Clear, Professor of Architecture, Dean of School of Arts and Humanities, University of Huddersfield Professor Nic Clear is a qualified architect, writer and curator. He is Professor of Architecture and Dean of the School of Arts and Humanities at the University of Huddersfield. He was a Professor of Architecture and Head of the Department of Architecture and Landscape at the University of Greenwich, having previously taught at the Bartlett School of Architecture for over 20 years. In 2015, he was the Inaugural Professor for Research in Visionary Cities at the Institute of Fine Arts in Vienna, and has taught in the UK, Europe, the US and Canada. Hyun Jun Park, Course Director Postgraduate Architecture, Leeds Beckett University Hyun Jun Park is a practitioner, writer, curator, fellow of the Royal Society of Arts and Course Director for Postgraduate Architecture at the Leeds School of Architecture, Leeds Beckett University. Prior to this, he was Course Leader for the Master of Architecture at the University of Huddersfield and taught postgraduate M.Arch design studio at the University of Greenwich. Before he came to the UK, he was an associate architect at SAMOO Architects & Engineers (SAMSUNG Corp), Seoul, Korea. He was awarded M.Arch by the Bartlett School of Architecture and finished his BA and first master’s degree at Hongik University, Seoul, Korea.Clear + ParkClear+Park use 3D laser scanning to create multidisciplinary works that operate across architecture, installation, and media arts. Clear+Park use 3D scanning to capture spaces and create spatial representations and narratives that engage with, and respond to specific site histories and spatial practices. Through their research Clear+Park explore ways in which architects and artists can reproduce, develop, manipulate, and represent spaces using advanced digital technology in ways that engage with non-specialist audiences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.254
Teacher spread0.250 · 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
GenreEditorial

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
Published2025
Admission routes1
Has abstractyes

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