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Record W4238446413 · doi:10.32920/ryerson.14647308

Electronic Waste & The Production Of Space

2021· preprint· en· W4238446413 on OpenAlexaff
Ron Philipp Noble

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsObsolescenceArchitectureElectronic wasteConsumption (sociology)Production (economics)UrbanizationBusinessSpace (punctuation)Value (mathematics)ElectronicsEngineeringWaste managementEnvironmental economicsArchitectural engineeringEconomicsComputer scienceGeographySociologyMarketingEconomic growthArchaeology

Abstract

fetched live from OpenAlex

The future of waste is electronics. The conditions of planned obsolescence combined with our throw away culture of capitalistic consumption has created the largest and fastest growing waste stream responsible for spatially transforming environments. Through the process of reclaiming precious materials contained within our dysfunctional electronics, urban mining becomes a form of resistance to the economics of consumption by recognizing electronic waste as a resource and turning its perceived detritus into value. If waste is central in the processes of capitalist urbanization, can architecture improve the condition of configuring industrial form to create ecology between e-waste, culture, and urbanity? Are there opportunities for e-waste and its architecture to have a public value and legibility in the city? Within this space of speculation, this thesis will explore the untapped architectural possibilities associated with the management of electronic waste and the production of space.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.005

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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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