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Enregistrement W6930516143 · doi:10.5281/zenodo.12805534

Data for Policy 2024 Book of Abstracts

2024· other· en· W6930516143 sur OpenAlexfundno aff

Notice bibliographique

RevueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2024
Typeother
Langueen
DomaineEarth and Planetary Sciences
ThématiqueEvolution and Paleontology Studies
Établissements canadiensnon disponible
Organismes subventionnairesUniversiteit LeidenJoint Research CentreUniversity of Hong KongYork UniversityEuropean CommissionUniversity of OttawaFlorida State University
Mots-clésPublic sectorGovernment (linguistics)Corporate governanceAgency (philosophy)Public policyIdentification (biology)Digital transformationSensibilityDigital government

Résumé

récupéré en direct d'OpenAlex

The need of a successful transition of public administrations towards the digital government ideal model increasingly compels public administrations worldwide to address challenges that stand far beyond the dimensions of organizational and technological innovation (Leoni et al., 2023). Earlier e-government studies emphasized the importance of intragovernmental integration of public information systems and of specific ICT-centered solutions (Charalabidis et al., 2019). Today, a more contemporary sensibility seems to reinforce the need to look outside governmental boundaries in the digital transformation of the public sector (Ravšelj et al., 2022). In fact, whilst boasting of positive potential to transform the paradigm of public, digital transformation exhibits the markings of a socio-technical problem, i.e., a problem that speaks to all public issues of high impact that are void of potential for incisive problem identification and solving (Rittel & Webber, 1973). Data-centric public services and AI-based solutions in the public sector are, therefore, increasingly addressed as socio-technical challenges that require broad-level considerations on data ethics, algorithmic legibility, social acceptance of technology, and coordination across public bodies. It is expected that new forms of collaboration between government and other societal actors will emerge based on organizational and semantic interoperability, thus suggesting the need to experiment with new forms of governance based on co-designed and participated processes with the ecosystem of stakeholders and beneficiaries (citizens). However, the public sector is still characterized by a functional 'silos' model, with a fragmentation of competencies and mandates (in Italy, for example, the National Statistical Agency counted more than 12,800 public bodies in a 2017 census). Several analyses carried out by international observatories indicate that the adoption of digital and data-driven solutions can improve the productivity and resilience of the public sector, as well as the perceived quality of its services (Ubaldi et al., 2019) when accompanied by horizontal organizational integration based on new institutional formulas, coordination mechanisms and policy tools that support a whole-of-government approach to digital governance (Dener et al., 2021). Europe is encouraging this perspective with a series of dedicated strategies, which foster collaborative governance among public actors, inclusive towards other social partners, especially towards citizens, the ultimate beneficiaries of the digital transformation in PA (e.g., Data Governance Act). In this sense, it is also worth mentioning The European Digital Rights and Principles and the EU 2030 Policy Programme, whose vision of digital transformation is functional to a transition to a climate-neutral, circular, and resilient economy, to be achieved by "[...] pursue digital policies that empower people and businesses to seize a human centred, sustainable and more prosperous digital future." (EC, 2021, p.1). In response to these challenges, public sector innovation labs (PSI Labs) or policy labs have been introduced in many countries, whose purpose is to research and test innovative practices and approaches for the transformation of the public system (McGann et al., 2021). In recent years, this phenomenon has become increasingly widespread internationally with different models of action, often as organizational units (teams) within the public sector function with a specific mandate to experiment with new forms of innovation related to governance and services. There are now several concrete examples of PSI Labs in various national states, implemented both within national ministries and agencies (e.g., the Laboratorio de Gobierno in Chile or LabX in Portugal) and in public and territorial agencies (e.g., the 27eme Région in France). In this sense, rather than identifying an absolute typology, PSI Labs seems to obey organizational constraints and opportunities peculiar and contextual to the ecosystems of subjects and practices in which they are introduced (Lindquist & Buttazzoni, 2021). Their establishment should, therefore be understood starting from a precise relation with a given institutional/public context. On these premises, this paper proposes a study of PSI Labs as-an-approach; in other words, PSI Labs as an action of governmental bodies toward public sector digital transformation. While several mapping and listing of PSI Labs exist, little research that concentrates on how PSI labs can be used to address the complexities of digital transitions while affecting policymaking (Carstens, 2023; Kim et al., 2022; Sandoval-Almazan & Millán-Vargas, 2023). To investigate this background, we ask the following: (RQ1) What are the main typologies of projects undertaken by PSI labs dealing with digital transformation at the central government level? (RQ2) What are the main characteristics of PSI lab as-an-approach to data/AI-centric innovations in the public sector? (RQ3) How are public bodies influencing policymaking through digital government initiatives by adopting PSI lab as-an-approach? To answer these questions, we developed a qualitative analysis of desk research data regarding the project portfolio of 6 PSI Labs working within, or in close relation with, the central government (i.e., public agencies or in-line departments) across 6 different European countries (Germany, Portugal, Norway, United Kingdom, France, Scotland).

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,741
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,002
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,059
Tête enseignante GPT0,284
Écart entre enseignants0,226 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2024
Routes d'admission1
Résumé présentoui

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Même revueVirtual Community of Pathological Anatomy (University of Castilla La Mancha)Même sujetEvolution and Paleontology StudiesTravaux en français237 207