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Enregistrement W6965097366 · doi:10.26207/43qj-sc32

Complex Governance Networks: A Comparative and Longitudinal Analysis of the Literature

2023· article· en· W6965097366 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholarSphere (Penn State Libraries) · 2023
Typearticle
Langueen
DomaineComputer Science
ThématiqueComputational Physics and Python Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésConceptualizationCorporate governanceSet (abstract data type)Empirical researchConceptual frameworkNetwork governancePublic policy

Résumé

récupéré en direct d'OpenAlex

Governance networks have been recognized and studied systematically by public administration researchers since the 1990s. The implications of complexity theory for governance networks entered the academic discourse in the 2000s. An integrated “complex governance networks” (CGN) conceptualization has not been articulate yet, however. In this presentation, we make the case for a CGN conceptualization with a systematic review of the literatures in public administration and related fields. In our review, we demonstrate that public administration and policy scholars have been relatively late in recognizing the implications of complexity theory, but their publications have increasingly integrated complexity concepts with governance and networks concepts in the 2010s. In our literature review, we aimed to answer the following questions. 1. How does the public administration literature compare with the related fields in the applications of governance, networks, and complexity concepts? How did these applications change over time? 2. How were the keywords related to governance, networks, and complexity concepts distributed in the public administration literature? How were these keywords linked to each other in this literature? How did these distributions and links change over time? 3. What was the geographic distribution of the studies? What were topical areas? Were they conceptual discussions, or did the authors develop models or conduct empirical studies? What methods did they use in the empirical studies? To answer these questions, we collected the literature information from the Web of Science journal publications database, using a set of keywords related to governance, networks, complexity, and self-organization. We investigated particularly the literature in English on public administration (inclusive of public policy) and a group of fields of study that are related to public administration: business/management, ecology/environment. economics, education research, health policy and administration, international relations and political science, and regional and urban studies. After our initial screenings and data cleaning, our dataset included 8,164 articles that were published between 1990 and 2019. We grouped and coded the articles in this final data set and conducted analyses in three stages: (1) we tabulated the frequencies of the keywords as they appeared in article titles and abstracts over the three decades in all the fields included in our study (research question #1), (2) we grouped and tabulated the keywords and conducted two-mode network analyses to identify the distributions of the keywords and the links between the keywords in the public administration literature (research question #2), and (3) we analyzed the contents of (a) the most frequently cited articles in public administration and (b) the “bridge articles” (articles that used more than one of the keywords in our study) to identify the distributions of the geographical locations and topical areas of the studies and the methods used in the public administration literature (research question #3). Our reviews show that public administration researchers lagged behind the researchers in business/management, ecology/environment, economics, and political science/international relations in their applications of complexity theory concepts to study collaborative/governance networks. However, these applications have increased in public administration over time. Also, complexity theory concepts were linked to governance networks and collaborative governance, particularly in the 2010s. Our reviews also show that the concepts of governance, networks, and complexity were applied in a wide spectrum of topical areas—from environment, to education, local government, economic development, emergency/disaster management, healthcare, water management, and democracy—and in studies worldwide—most frequently in the US and European countries, but also in Canada and some countries in Asia, Africa, and Oceania. These applications in wide ranges of topical areas and geographies show that these concepts have almost universal applicability. Our findings on the methodological applications indicate that the applications of these concepts began to mature in the sense that they moved from conceptual discussions only to refinements of conceptualizations (modeling) and empirical investigations of specific aspects of governance processes and networks. The most frequently used methodologies in the earlier publications were qualitative case studies and interviews; over time the applications of quantitative methods (particularly SNA methods and computational modeling) increased. This finding indicates that researchers were seeking to find generalizable patterns and structures in networks. Our findings support our contention that there is a need and grounding for a complex governance networks (CGN) conceptualization. This conceptualization is presented in a recently published book (Morçöl, 2023) and an upcoming journal article. Reference Morçöl, G. (2023). Complex governance networks: Foundational concepts and practical implications. New York: Routledge.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,056
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,056
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0240,029
Études des sciences et des technologies0,0030,002
Communication savante0,0040,011
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,274
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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