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Record W4392983872 · doi:10.1080/13501763.2024.2325647

Hybrid organisations and governance systems: the case of the European Space Agency

2024· article· en· W4392983872 on OpenAlexafffund
Guillaume Beaumier, Cynthia Couette, Jean‐Frédéric Morin

Bibliographic record

VenueJournal of European Public Policy · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsUniversité LavalÉcole Nationale d'Administration Publique
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCorporate governanceAgency (philosophy)Multi-level governanceSpace (punctuation)Public administrationEuropean unionBusinessPolitical scienceEconomic systemEconomicsSociologyInternational tradeComputer scienceFinance

Abstract

fetched live from OpenAlex

The constitutive organisations of governance systems tend to multiply and diversify over time. In parallel, a tendency toward homophily favours the creation of clusters of homogeneous organisations. Yet, few systems drift to the point of disconnection or dislocation. Several remain sufficiently cohesive to allow adaptation and other complex properties to emerge. To maintain equilibrium between order and chaos, some organisations must create bridges between otherwise homogeneous groups. This paper argues that hybrid organisations are ideally suited for this role. By their nature, hybrids share characteristics with different types of organisations in global governance, allowing them to overcome strict homophily and create bridges across clusters. Hybrids benefit from acting as brokers and in doing so, they facilitate the exchange of material and ideational resources across the governance system. Even if it is not their intention, they contribute to holding governance systems together and counterbalance the effect of homophily. We illustrate this argument by examining the space governance system and the hybrid nature, bridging activities, and brokerage role of the European Space Agency.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.246

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 studies0.0000.000
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 head0.016
GPT teacher head0.248
Teacher spread0.231 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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