MétaCan
Menu
Back to cohort
Record W4396893891 · doi:10.1353/cot.2023.a927231

More Than Urban Mining: Salvaging Modern Material Discards for Meaningful Reuse

2023· article· en· W4396893891 on OpenAlexaboutno aff
Susan M. Ross

Bibliographic record

VenueChange Over Time · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsReuseDiscardsPolitical scienceLawEngineeringWaste management

Abstract

fetched live from OpenAlex

Abstract: As redevelopment of already built-up sites expands in North American cities, tearing down large modern buildings is also accelerating. In pursuit of circular economy and sustainable building ideals, traditional practices like deconstruction for salvage and reuse are being revived. However, a shift toward valuing existing buildings as material banks, as implied in the rise of "urban mining," challenges established material heritage values. Attempts to salvage and reuse modern materials and assemblies raise specific issues, notably the perceived obsolescence of industrial materials and modern buildings and the complex and difficult values of hazardous or experimental elements. In some cases, reinventing modern materials with new uses in new places will expand or reframe heritage values while helping us learn how to address their challenges as objects of materials conservation. To evaluate current practices, this paper discusses selected examples from Ottawa's postwar urban landscape, each of which illustrates the range of issues for alternatives to demolition. Specific opportunities for modern heritage include reorienting salvage efforts to the scale of the component and redefining materials reuse as a critique of modernist models of obsolescence and materials waste.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueChange Over TimeSame topicRecycling and Waste Management TechniquesFrench-language works237,207