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Record W6930516143 · doi:10.5281/zenodo.12805534

Data for Policy 2024 Book of Abstracts

2024· other· en· W6930516143 on OpenAlexfundno aff

Bibliographic record

VenueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2024
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
FundersUniversiteit LeidenJoint Research CentreUniversity of Hong KongYork UniversityEuropean CommissionUniversity of OttawaFlorida State University
KeywordsPublic sectorGovernment (linguistics)Corporate governanceAgency (philosophy)Public policyIdentification (biology)Digital transformationSensibilityDigital government

Abstract

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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).

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.323
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.015
Science and technology studies0.0010.001
Scholarly communication0.0110.006
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.6770.625

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 head0.059
GPT teacher head0.284
Teacher spread0.226 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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