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Record W3104074387 · doi:10.1139/facets-2020-0031

Transforming conflict over natural resources: a socio-ecological systems analysis of agricultural drainage

2020· article· en· W3104074387 on OpenAlexafffundvenueabout
Sarah Minnes, Valencia Gaspard, Philip A. Loring, Helen M. Baulch, Sarah‐Patricia Breen

Bibliographic record

VenueFACETS · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of SaskatchewanUniversity of Guelph
FundersArrell Food Institute, University of GuelphGlobal Water FuturesCanada First Research Excellence FundUniversity of Guelph
KeywordsConflict transformationNatural resourceNatural resource managementEcosystem servicesConflict managementAgricultureSocial conflictEmpowermentEnvironmental resource managementResource (disambiguation)SociologyPolitical scienceEnvironmental planningEcologyGeographyEcosystemSocial scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

Agricultural drainage is a complicated and often conflict-ridden natural resource management issue, impacting contested ecosystem services related to the retention of wetlands as well as the productivity of farmland. This research identifies opportunities to transform the conflict over agricultural drainage in Saskatchewan, Canada, towards collaboration. We report on ethnographic research informed by a conservation conflict-transformation framework to evaluate the nature of the conflict and whether drivers of the conflict operate principally at the level of disputes over discrete ecosystem services or if they reach deeper into local social circumstances and build on larger unresolved conflict(s) among groups in the region. In addition to the conflict-transformation framework, we apply the Social–Ecological Systems Framework to elicit details regarding the substantive, relational, and material dimensions of this conflict. Our research suggests that processes for governing natural resources, such as those in place for governing drainage in Saskatchewan, need to have mechanisms to facilitate relationship building and shared understandings, need to be adaptable to people’s changing needs and concerns, and should focus on inclusivity and empowerment of actors to address conflict.

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 score0.032
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.211
Teacher spread0.192 · 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

Citations14
Published2020
Admission routes4
Has abstractyes

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