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Record W2590079309 · doi:10.1093/icesjms/fsx009

Shaping the future of marine socio-ecological systems research: when early-career researchers meet the seniors

2017· article· en· W2590079309 on OpenAlexaff
Evangelia G. Drakou, Charlène Kermagoret, Adrien Comte, B.K. Trapman, Jake Rice

Bibliographic record

VenueICES Journal of Marine Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
FundersUniversité de Bretagne OccidentaleAgence Nationale de la Recherche
KeywordsEcological systems theoryCorporate governanceSociologyPoliticsEngineering ethicsPragmatismEcological psychologyPolitical scienceEcologyEnvironmental resource managementPublic relationsPsychologyBusinessEngineering

Abstract

fetched live from OpenAlex

Abstract As the environmental issues facing our planet change, scientific efforts need to inform the sustainable management of marine resources by adopting a socio-ecological systems approach. Taking the symposium on “Understanding marine socio-ecological systems: including the human dimension in Integrated Ecosystem Assessments (MSEAS)” as an opportunity we organized a workshop to foster the dialogue between early and advanced-career researchers and explore the conceptual and methodological challenges marine socio-ecological systems research faces. The discussions focused on: a) interdisciplinary research teams versus interdisciplinary scientists; b) idealism versus pragmatism on dealing with data and conceptual gaps; c) publishing interdisciplinary research. Another major discussion point was the speed at which governance regimes and institutional structures are changing and the role of researchers in keeping up with it. Irrespective of generation, training or nationality, all participants agreed on the need for multi-method approaches that encompass different social, political, ecological and institutional settings, account for complexity and communicate uncertainties. A shift is needed in the questions the marine socio-ecological scientific community addresses, which could happen by drawing on lessons learnt and experiences gained. These require in turn a change in education and training, accompanied by a change in research and educational infrastructures.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0010.001
Open science0.0050.029
Research integrity0.0000.001
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 head0.126
GPT teacher head0.342
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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