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Record W4253097307 · doi:10.32920/ryerson.14647098.v1

Energanic prototypes in the [post]-digital terrain

2021· preprint· en· W4253097307 on OpenAlexaff
Andrew Kaleva Hotari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectureWorkspaceEnergy (signal processing)Process (computing)Work (physics)Computer scienceTerrainArchitectural engineeringHuman–computer interactionSociologyKnowledge managementData scienceEpistemologyPolitical scienceEngineeringArtificial intelligenceGeographyMechanical engineeringVisual artsMathematicsCartographyArt

Abstract

fetched live from OpenAlex

Although the direction of contemporary architectural thinking is heavily influenced by its critical engagement with energy usage, this relationship remains largely unexplored imaginatively. This thesis investigates an energy-centric approach to design that is enabled by digital workspace. By injecting energy transactions and modulations into otherwise abstract digital geometry while using analysis tools to examine their effects, the work is intended to speculate what this relationship with energy could be. For too long the application of emerging computer-based technologies in architecture have resisted critical agendas beyond idealist shape-making and form. At the same time the role of energy in the design process has been subsidiary and weak. Both fields of knowledge and their relationship to architecture are examined in a necessary marriage of mission and means. The research portion of this document concludes with a series of speculations that illustrate possible outcomes of the proposed energetic agenda.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

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 studies0.0020.005
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.002

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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