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Record W4404527419 · doi:10.4324/9781003366133-24

Blood, mud, and mucking around with waste

2024· book-chapter· en· W4404527419 on OpenAlexaboutno aff
Shannon A. Novak

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceGeographyPhysical geographyGeology

Abstract

fetched live from OpenAlex

This chapter explores the logic of separation practices at a mother goddess temple ( mandir ) in the industrial outskirts of Toronto, Canada. Such logic, importantly, has material-real implications for the gendering of landscapes, bodies, and subjectivities. Over the past four decades a growing community of Indo-Guyanese Hindus have settled in this area, some bringing with them a healing cult centered on a female deity, Mariamma . Carried to the Caribbean in the nineteenth century by indentured laborers from India, these practices have traversed diverse colonial and postcolonial landscapes. Relevant are concerns with re production as opposed to mere production under extractive, capitalist regimes. Such “work” requires assembling with abundance, resulting in copious byproducts and waste; these materials, however, are not all treated the same. Whether shared, recycled, or cast “away,” these acts have consequences for devotees who characterize themselves as “unclean.” Implicated is menstrual blood, some “thing” used to differentially separate some people and prohibit where they can be. Drawing on insights from waste and discard studies, and ecofeminist concepts of “reworlding,” I explore the power of such matters to denaturalize binary and hierarchal categories of difference manipulated under colonial regimes. At the mandir in Brampton, women are disrupting perfectionist ontologies, even while adopting the language of recycling campaigns. Still others are pushing boundaries, taking advantage of spaces that open up, and empowering (un)becoming associations iwith 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.662
Threshold uncertainty score0.680

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 studies0.0210.042
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.078
GPT teacher head0.384
Teacher spread0.307 · 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
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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