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Record W2466690303 · doi:10.1057/eps.2016.26

Mapping the ‘Enviro-Security’ Field: Rivalry and Cooperation in the Construction of Knowledge

2016· article· en· W2466690303 on OpenAlexaff
Sarah Saublet, Vincent Larivière

Bibliographic record

VenueEuropean Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRivalryField (mathematics)Citation analysisCitationPolitical scienceRegional scienceSociologyData scienceSocial scienceKnowledge managementEpistemologyComputer scienceLawEconomics

Abstract

fetched live from OpenAlex

Abstract This paper maps out the network of researchers on environmental security from the end of the 1980s to 2014, providing a systematic analysis of how the research is organized in this interdisciplinary field. Drawing on the Web of Science database, we generated a co-citation analysis that exhibits the cognitive structure of the field. Twenty interviews were conducted in order to uncover relationships of cooperation, rivalry and conflict so as to understand the structure of academic debates inside the field over time. The research findings highlight that central authors have had a long-lasting influence on the field despite the evolution of their productivity. We also found that the field is composed of six fairly structured groups as well as a few peripheral authors, which can in turn be distinguished by their epistemological and methodological choices, as well as geographical centre of gravity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0230.020
Science and technology studies0.0030.006
Scholarly communication0.0070.009
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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