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Record W3128158625 · doi:10.26522/ssj.v15i1.2435

Constructing Another World: Solidarity and the Right to Water

2021· article· en· W3128158625 on OpenAlexvenueno aff
Caitlin Schroering

Bibliographic record

VenueStudies in Social Justice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsSolidarityCommonsDemocracyPolitical economySocial movementSociologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Globally, one in eight people lacks access to potable water; more people die from unsafe drinking water than from all forms of violence, including war. A substantial body of research documents that the privatization of water – led by global financial institutions working in collusion with governments and corporations – does not lead to more people gaining access to safe water. In fact, the opposite is true: privatization leads to both higher cost and lower quality water. For the past century, the dominant focus of transnational organizing has been “from the West to the rest,” and the frequent attention to movements in the global North has led to the neglect of transnational linkages between movements. Drawing on fieldwork conducted on three right to water movements that span three continents (North America, South America, and Africa), this paper examines effortsto reclaim the water commons,and how struggles have been driven by grassroots movements demanding that democracy, transparency, and the human right to water are prioritized over corporate profit. As feminist scholars have pointed out, the “standpoint” offered by marginalized actors offers important insights into the operation of systems of power and the strategies of survival and resistance that less powerful actors adopt in order to survive and thrive. This paper explores how transnational movements around water and other basic rights engage with and learn from each other.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.346
Teacher spread0.318 · 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 designQualitative
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

Citations7
Published2021
Admission routes1
Has abstractyes

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