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Record W7070420525

The pedagogies of reuse

2024· article· en· W7070420525 on OpenAlexaboutno aff

Bibliographic record

VenueRoyal College of Art Research Repository (Royal College of Art) · 2024
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)ArchitectureReuseIdeal (ethics)Reading (process)Circulation (fluid dynamics)
DOInot available

Abstract

fetched live from OpenAlex

The Pedagogies of Re-Use captures the amazing digital gathering of students, academics, practitioners, and activists that happened at the International School of Re-Construction. Involving over 100 people, from countries as far apart as Brazil, Canada, Ireland, UK, Spain, Germany, Greece, UAE, and China, the participants spent two weeks working in eleven teams to consider architectural propositions responding to the current climate and ecological emergency. This book documents the work of the eleven teams, considering the themes they pursued, the student projects proposed, and the final design ideas developed by each group. Supplemented with images of the work, the book also includes leading academics and professionals who supported the school and contribute their voices to these crucial issues of deconstruction, re-use, and adaptation. It is ideal reading for students and academics looking at the issues created by the climate emergency to which architecture must respond. The Pedagogies of Re-Use is part of an EU ERDF £4.33 million Interreg NWE project entitled ‘Facilitating the Circulation of Reclaimed Building Elements’ (FCRBE), Interreg NWE 739, October 2018– December 2023. Online publication: June 2024, London. The FCRBE project aims to increase the amount of reclaimed building elements in circulation within its territory by +50% (in mass) by 2032.

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)0.008
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.044
Scholarly communication0.0120.019
Open science0.0020.012
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.003

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 head0.030
GPT teacher head0.305
Teacher spread0.275 · 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
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
Published2024
Admission routes1
Has abstractyes

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