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‘Safe space’ as counter‐space: women, environmental illness and ‘corporeal chaos’

2004· article· en· W2022989196 on OpenAlexvenueno aff
Fiona Coyle

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)Construct (python library)CHAOS (operating system)Sociology of health and illnessEnvironmental ethicsPsychologyComputer scienceLawComputer securityPolitical scienceHealth carePhilosophy

Abstract

fetched live from OpenAlex

Women constitute a disproportionate 80 percent of people diagnosed with environmental illness (EI), a contentious condition in which patients react adversely to everyday chemicals in the environment at levels politically conceived to be ‘safe’. Whilst the diverse range of somatic symptoms constitutes a biomedical anomaly, in this paper I present an alternative means of conceiving environmentally ill bodies. Women (and environmental health practitioners at the Environmental Health Centre, Nova Scotia) have begun to view their bodies as complex systems that have been nudged into a state of ‘corporeal chaos’, in which minute quantities of chemicals trigger disproportionate somatic symptoms. This chaos extends into ‘corporeal space’[ Moss and Dyck (1999a) ] as the diagnosis of environmental illness is experienced simultaneously through both material and discursive bodies. This diagnosis also carries with it a means to mitigate corporeal chaos through a series of body‐ and environment‐based modifications that replace risky bodies with ‘safe space’. As a discursive construct, safe space is associated with an absence of chemicals, and in order to mitigate chaos, should ideally be stable, predictable, controllable and communicative. I finalise this paper with some examples of body modifications and illustrate how safe space materialises in the home environment .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0050.008
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations32
Published2004
Admission routes1
Has abstractyes

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