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Record W4312190462 · doi:10.1177/25148486221143666

Explaining societal change through bricolage: Transformations in regimes of water governance

2022· article· en· W4312190462 on OpenAlexaff
Pierre–Louis Mayaux, Muna Dajani, Frances Cleaver, Mohamed Naouri, Marcel Kuper, Tarik Hartani

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsLethbridge College
FundersEconomic and Social Research CouncilAgence Nationale de la RechercheGlobal Challenges Research Fund
KeywordsBricolageCorporate governanceSustainabilityAgricultureState (computer science)SociologyPolitical scienceEconomic systemSocial scienceEconomicsManagementEcologyComputer science

Abstract

fetched live from OpenAlex

This paper is motivated by the pressing need to understand how water use and irrigated agriculture can be transformed in the interests of both social and environmental sustainability. How can such change come about? In particular, given the generally mixed results of simplified, state-initiated projects of social engineering, what is the potential for transformations in societal regimes of governance to be anchored in the everyday practices of farmers? In this paper, we address these enduring questions in novel ways. We argue that the concept of bricolage, commonly applied to analysing community management of resources, can be developed and deployed to explain broad societal processes of change. To illustrate this, we draw on case studies of irrigated agriculture in Saharan areas of Algeria and in the occupied Golan Heights in Syria. Our case analysis offers insights into how processes of institutional, technological and ideational bricolage entwine, how the state becomes implicated in them and how multiple instances of bricolage accumulate over time to produce meaningful systemic change. In concluding, however, we reflect on the greater propensity of contemporary bricolage to rebalance power relations than to open the way to more ecological farming practices.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.241

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 studies0.0000.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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