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Record W4205669229 · doi:10.3390/su14020610

Shock and Conflict in Social-Ecological Systems: Implications for Environmental Governance

2022· article· en· W4205669229 on OpenAlexaff
Kristof Van Assche, Mónica Gruezmacher, Raoul Beunen

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
Fundersnot available
KeywordsCorporate governanceShock (circulatory)RestructuringFlexibility (engineering)EcologyEconomic systemSocial systemEconomicsPolitical scienceSociologyBiologySocial scienceLaw

Abstract

fetched live from OpenAlex

In this paper, we present a framework for the analysis of shock and conflict in social-ecological systems and investigate the implications of this perspective for the understanding of environmental governance, particularly its evolutionary patterns and drivers. We dwell on the distinction between shock and conflict. In mapping the relation between shock and conflict, we invoke a different potentiality for altering rigidity and flexibility in governance; different possibilities for recall, revival and trauma; and different pathways for restructuring the relation between governance, community and environment. Shock and conflict can be both productive and eroding, and for each, one can observe that productivity can be positive or negative. These different effects in governance can be analyzed in terms of object and subject creation, path creation and in terms of the dependencies recognized by evolutionary governance theory: path, inter-, goal and material dependencies. Thus, shock and conflict are mapped in their potential consequences to not only shift a path of governance, but also to transform the pattern of self-transformation in such path. Finally, we reflect on what this means for the interpretation of adaptive governance of social-ecological systems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.654

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 studies0.0010.000
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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