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Record W1586311915 · doi:10.29173/cjs19176

Mascarenhas, Michael, Where the Waters Divide: Neoliberalism, White Privilege, and Environmental Racism in Canada.

2013· article· en· W1586311915 on OpenAlexaffvenueabout
Margot Francis

Bibliographic record

VenueThe Canadian Journal of Sociology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsBrock University
Fundersnot available
KeywordsNeoliberalism (international relations)RacismSociologyEnvironmental justiceIndigenous rightsPrivilege (computing)White supremacyGender studiesPoliticsWhite privilegeEnvironmental ethicsGovernmentalityLawPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Mascarenhas, Michael, Where the Waters Divide: Neoliberalism, White Privilege, and Environmental Racism in Canada. Toronto: Lexington Books, 2012, 161 pp., $60.00 (978-0-7391-6827-1) In Where the Waters Divide, Michael Mascarenhas has provided a detailed set of case studies which explore the racial structure of water governance in Ontario. The great strength of this book is that it systematically names and explores how neoliberalism is a racial formation highlighting not simply how water policy disadvantages First Nations communities but also how concomitant water practices privilege the environmental and social conditions that white Canadians take for granted. Mascaren draws on the literatures regarding environmental justice, social reproduction, and critiques of neoliberal reform, and incorporates ethnographic interviews with twenty-seven participants, of whom work in or with six First Nations communities in southern Ontario. The author highlights the history of dispossession, water diversion, and dam projects that have characterized Canadian-First Nations relationships, while demonstrating how contemporary technologies of administration, expertise, and entrepreneurialism are reinscribing deeply hierarchical racial inequalities. Through four case studies (from the dispossession of the Akwesasne Mohawk Nation on the St. Lawrence River to the contemporary practices of technoscientific knowledge that remove issues of power and equity from broader environmental concerns) Mascarenhas shows how neoliberal water policy enacts a form of racism without racists insofar as it ensures that poor, minoritized, and especially Indigenous communities carry the burden of being unable to access clean and safe water. Mascarenhas' research was inspired, in part, by a little reported but key finding from the Walkerton reports which observed that First Nations reserves had some of the poorest quality drinking water in the province. The question driving the book is how the stunning inequality between Indigenous and non-Indigenous communities in their access to clean water has been normalized, or to use a phrase from W.E.B. Dubois, has come to be viewed with a kind of peculiar indifference (Dubois 1899:157 quoted on p. 145). I highlight two examples to illustrate the major themes and contributions of the book. Statistics from the Minister of Supply and Services Canada confirm, most of the water treatment plans on First Nations reserves in Ontario are in need of repair, upgrading or replacement (p. 21). While the regulatory regime governing water quality on reserves is structured through multiple and overlapping departments, the primary branch responsible for drinking water services is Indian and Northern Affairs Canada (INAC). Through analysis of the Walkerton reports and interviews with First Nations water technicians Mascarenhas details INAC's approach to this widely acknowledged, yet stubbornly persistent problem. His analysis suggests that INAC fails to consult with the very people responsible for water management on reserves, and often takes years to approve applications to repair failing infrastructure. As federal and provincial departments have been cut back, audit type practices have become a common strategy for monitoring water quality in municipal and reserve contexts. However, many of the experienced First Nations water operators believe that this shift from service provider to service supervisor modes of governance has less to do with the improvement of water quality and more to do government's efforts to distance itself from what they saw as highly political and racial decisions that border on dereliction of government responsibility (p. …

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.733

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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 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

Citations0
Published2013
Admission routes3
Has abstractyes

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