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Record W142839164 · doi:10.1177/030437540302800301

From EURATOM to “Complex Systems”: Technology and European Government

2003· article· en· W142839164 on OpenAlexaff
Andrew Barry, William Walters

Bibliographic record

VenueAlternatives Global Local Political · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsGovernment (linguistics)Political scienceSystems engineeringEngineeringPhilosophyLinguistics

Abstract

fetched live from OpenAlex

In a recent series of papers written for the European Commission's Cellule de Prospective (CdP), Notis Lebessis and others have begun to think about the European Union - its political future, its accomplishments, and the challenges facing it - in terms of a social condition they define as complexity. In brief, the CdP suggests that the EU represents a peculiarly inventive and functional response to the increasingly complex environment within which modern government must operate. As social and economic life has become more complex, the CdP argues, the forms of network organization associated with the European Union have become more and more relevant. challenges presented by contemporary society in terms of complexity, diversity and interdependency mean that these traditional forms [e.g., national parliamentary politics] are stretched beyond their limits and that new forms begin to emerge.1 At the same time, knowledge has acquired a particular centrality to the constitution of the contemporary European system of government. The role of the public authorities is to escape from the constraints of the institutional (bureaucrat/ expert) construction of problems and solutions.2 The task of the EU is to encourage participation and to foster an economy based on collective learning. The conclusion is intended to be both descriptive and normative. The CdP's reports both account for the development of the EU in the past and provide blueprints for the future. Their publication (both internally and on the internet) is intended to be much more than an analysis of EU government - to be, indeed, a significant political event itself.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0030.029
Scholarly communication0.0120.015
Open science0.0010.005
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.322
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations26
Published2003
Admission routes1
Has abstractyes

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