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Record W7118169369 · doi:10.1080/10464883.2025.2600885

In Conversation with Johanna Hurme and Sasa Radulovic

2025· article· en· W7118169369 on OpenAlexaboutno aff
Michelangelo Sabatino, Rafael Longoria

Bibliographic record

VenueJournal of Architectural Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDecolonial Thought and Epistemologies
Canadian institutionsnot available
Fundersnot available
KeywordsConversationSasaConversation analysisEthnography

Abstract

fetched live from OpenAlex

Johanna Hurme is an architect, cofounder and managing partner of 5468796 Architecture. Originally from Helsinki, Finland, Hurme received her architectural education at the Aalto University in Helsinki and at the University of Manitoba (Bachelor of Environmental Design 1999, Master of Architecture 2002) after emigrating to Canada. She cofounded 5468796 Architecture with Sasa Radulovic in 2007. With its beginnings in student design competitions, Hurme’s design partnership with Radulovic spans nearly three decades, resulting in some of the firm’s most seminal projects. She brings conceptual depth and a focus on how spaces and places shape human experience. Influenced by Nordic sensibilities, her approach emphasizes clarity and restraint, reducing projects to their essence and always seeking for the “just enough.” She has also shaped the firm’s ethos, broadening architectural practice to intersect with politics, economics, social activism, cultural research, and pragmatic engagement.Sasa Radulovic is an architect, cofounder and partner at 5468796 Architecture. Originally from Sarajevo, the former Yugoslavia, Radulovic was educated in Sarajevo, Belgrade, and at the University of Manitoba (Bachelor of Environmental Design 1999, Master of Architecture 2003) after escaping the Sarajevo and Balkan war during the mid 90s. He cofounded 5468796 Architecture with Johanna Hurme in 2007. As an exceptionally accomplished designer and an avid follower of contemporary architectural work around the globe, Radulovic spearheads the firm’s artistic output through a critical lens on current movements, issues, and contexts, grounded on deep technical understanding of buildable solutions and real-life cost implications. His relentless pursuit of a new language and ‘architecture of consequence’ has driven the firm’s work to be recognized with numerous national and international awards and inclusion in publications around the world.

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)0.003
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0170.005

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 head0.005
GPT teacher head0.331
Teacher spread0.326 · 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
GenreOther

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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