MétaCan
Menu
Back to cohort
Record W4286689256 · doi:10.1111/apaa.12159

6 The Ecological Life of Industrial Waste

2022· article· en· W4286689256 on OpenAlexaboutno aff
Haeden Stewart

Bibliographic record

VenueArcheological Papers of the American Anthropological Association · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneIndustrial ecologyIndustrial RevolutionEnvironmental historyIndustrial wasteArchaeologyHistoryEcologySustainabilityEngineeringWaste management

Abstract

fetched live from OpenAlex

ABSTRACT The post‐depositional afterlife of an archaeological site is often viewed as the least important aspect of its history and outside of traditional archaeological interest. In the case of industrial sites, this elision ignores one of the most important aspects of industrial history, namely the long‐term effects of toxic waste. In an era where industrial pollution and anthropogenic climate change are rapidly changing the future of life on this planet, the stakes of understanding the effects of industrial waste are vital. This article outlines a reflexive, ecologically focused archaeology that interrogates the afterlives of industrial waste, not as a method to get back to the history of production, but as a means for taking seriously these afterlives as a defining characteristic of life in the Anthropocene. Using the concept of the ecological lives of industrial waste to explore the (post)industrial history of Mill Creek Ravine—a historically important industrial area in Edmonton, Alberta— this article argues that the decomposition of industrial waste serves as both a medium for long‐term harms, as well as the locus for emergent relations and critical investigation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.022
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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 designObservational
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

Citations14
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueArcheological Papers of the American Anthropological AssociationSame topicHistorical and Cultural Archaeology StudiesFrench-language works237,207