Hasil untuk "Public law"

H. Morgenthau

M. Foucault

James F. Wilson

T. Anton, E. Bardach

J. Osofsky

Mancur Olson

Sherzod Turaev, Mary John

Public space quality assessment lacks systematic methodologies that integrate factors across diverse spatial typologies while maintaining context-specific relevance. Current approaches remain fragmented within disciplinary boundaries, limiting comprehensive evaluation and comparative analysis across different space types. This study develops a systematic, data-driven framework for assessing public space quality through the algorithmic integration of empirical research findings. Using a 7-phase methodology, we transform 1,207 quality factors extracted from 157 peer-reviewed studies into a validated hierarchical taxonomy spanning six public space typologies: urban spaces, open spaces, green spaces, parks and waterfronts, streets and squares, and public facilities. The methodology combines semantic analysis, cross-typology distribution analysis, and domain knowledge integration to address terminological variations and functional relationships across space types. The resulting framework organizes 1,029 unique quality factors across 14 main categories and 66 subcategories, identifying 278 universal factors applicable across all space types, 397 space-specific factors unique to particular typologies, and 124 cross-cutting factors serving multiple functions. Framework validation demonstrates systematic consistency in factor organization and theoretical alignment with established research on public spaces. This research provides a systematic methodology for transforming empirical public space research into practical assessment frameworks, supporting evidence-based policy development, design quality evaluation, and comparative analysis across diverse urban contexts.

Timo Kuosmanen, Xun Zhou

This paper critically investigates standard total factor productivity (TFP) measurement in the public sector, where output information is often incomplete or distorted. The analysis reveals fundamental paradoxes under three common output measurement conventions. When cost-based value added is used as the aggregate output, measured TFP may paradoxically decline as a result of genuine productivity-enhancing changes such as technical progress and improved allocative and scale efficiencies, as well as reductions in real input prices. We show that the same problems carry over to the situation where the aggregate output is constructed as the cost-share weighted index of outputs. In the case of distorted output prices, measured TFP may move independently of any productivity changes and instead reflect shifts in pricing mechanisms. Using empirical illustrations from the United Kingdom and Finland, we demonstrate that such distortions are not merely theoretical but are embedded in widely used public productivity statistics. We argue that public sector TFP measurement requires a shift away from cost-based aggregation of outputs and toward non-market valuation methods grounded in economic theory.

Riddhi Kalsi

This paper resolves the empirical puzzle in the public-private wage literature: why studies using similar data reach contradictory conclusions about wage premiums and penalties. Utilizing rich French administrative panel data (2012-2019), this study has two main contributions: first, it presents a set of new, intuitive yet previously undocumented stylized facts about wage dynamics, sectoral mobility, and gender differences across sectors. The results reveal that the modest hourly wage gaps conceal substantial disparities in lifetime earnings and employment stability. Women, in particular, gain a significant lifetime earnings advantage in the public sector, driven by higher retention, better-compensated part-time work, and more equitable annual hours compared to the private sector, where gender gaps remain larger, especially for those with higher education. In contrast, highly educated men experience a lifetime penalty in public employment due to rigid wage structures. By flexibly modeling sectoral transitions, transitions into and out of employment, and earnings heterogeneity using an Expectation-Maximization algorithm, this study shows that both premiums and penalties depend systematically on gender, education, and labor market experience. The analysis reveals that significant unobserved heterogeneity remains in wage dynamics. These findings unify prevailing narratives by providing a comprehensive, descriptive account of sectoral differences in transitions, part-time work and wages by gender.

Petia Guintchev, Joost J. Joosten, Sofia Santiago Fernández et al.

We speak of a \textit{computational law} when that law is intended to be enforced by software through an automated decision-making process. As digital technologies evolve to offer more solutions for public administrations, we see an ever-increasing number of computational laws. Traditionally, law is written in natural language. Computational laws, however, suffer various complications when written in natural language, such as underspecification and ambiguity which lead to a diversity of possible interpretations to be made by the coder. These could potentially result into an uneven application of the law. Thus, resorting to formal languages to write computational laws is tempting. However, writing laws in a formal language leads to further complications, for example, incomprehensibility for non-experts, lack of explicit motivation of the decisions made, or difficulties in retrieving the data leading to the outcome. In this paper, we investigate how certain legal principles fare in both scenarios: computational law written in natural language or written in formal language. We use a running example from the European Union's road transport regulation to showcase the tensions arising, and the benefits from each language.

Mª Ángeles Caraballo, Oksana Liashenko

Background Institutional quality is a critical determinant of development outcomes, yet the role of social attitudes in shaping institutions remains underexplored. This study examines the impact of public attitudes toward gender equality, environmental protection, and immigration on institutional strength and socioeconomic development. Method Using data from Wave 7 of the World Values Survey, we apply a classification of attitudes based on a combination of set theory and ordinal preference logic. Respondents are grouped into 27 attitude combinations and then aggregated into eight categories. Country-level proportions are computed. We apply Bayesian Network Analysis (BNA) to uncover complex dependencies, identifying relationships and central institutional nodes such as the rule of law, democratic stability, and market organisation. Latent institutional quality and development outcomes variables are derived using Principal Component Analysis. We then use Structural Equation Modelling (SEM) to test a mediation model, estimating direct and indirect effects of attitudes on development outcomes. Bootstrapping with 5,000 replications ensures statistical robustness. Results BNA reveals that institutional quality is a key bridge between social attitudes and development outcomes. SEM confirms that institutional quality mediates these effects in most cases. Neutral-positive and mixed-neutral attitudes yield the most potent positive indirect effects, underscoring their role in consensus building. Negative attitudes are associated with institutional weakening and lower development performance. Interestingly, moderately negative views may drive democratic reform when linked to institutional accountability. Conclusion Social attitudes affect development primarily through their influence on institutions. Contrary to common assumptions, moderate and neutral positions are not passive; they foster institutional adaptability and stability. These findings underscore the importance of targeting centrist groups in policy design to reinforce inclusive governance and long-term development.

H. Grotius

Andrew Konya, Aviv Ovadya, Kevin Feng et al.

We introduce a method to measure the alignment between public will and language model (LM) behavior that can be applied to fine-tuning, online oversight, and pre-release safety checks. Our `chain of alignment' (CoA) approach produces a rule based reward (RBR) by creating model behavior $\textit{rules}$ aligned to normative $\textit{objectives}$ aligned to $\textit{public will}$. This factoring enables a nonexpert public to directly specify their will through the normative objectives, while expert intelligence is used to figure out rules entailing model behavior that best achieves those objectives. We validate our approach by applying it across three different domains of LM prompts related to mental health. We demonstrate a public input process built on collective dialogues and bridging-based ranking that reliably produces normative objectives supported by at least $96\% \pm 2\%$ of the US public. We then show that rules developed by mental health experts to achieve those objectives enable a RBR that evaluates an LM response's alignment with the objectives similarly to human experts (Pearson's $r=0.841$, $AUC=0.964$). By measuring alignment with objectives that have near unanimous public support, these CoA RBRs provide an approximate measure of alignment between LM behavior and public will.

Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh

Local differential privacy (LDP) is increasingly employed in privacy-preserving machine learning to protect user data before sharing it with an untrusted aggregator. Most LDP methods assume that users possess only a single data record, which is a significant limitation since users often gather extensive datasets (e.g., images, text, time-series data) and frequently have access to public datasets. To address this limitation, we propose a locally private sampling framework that leverages both the private and public datasets of each user. Specifically, we assume each user has two distributions: $p$ and $q$ that represent their private dataset and the public dataset, respectively. The objective is to design a mechanism that generates a private sample approximating $p$ while simultaneously preserving $q$. We frame this objective as a minimax optimization problem using $f$-divergence as the utility measure. We fully characterize the minimax optimal mechanisms for general $f$-divergences provided that $p$ and $q$ are discrete distributions. Remarkably, we demonstrate that this optimal mechanism is universal across all $f$-divergences. Experiments validate the effectiveness of our minimax optimal sampler compared to the state-of-the-art locally private sampler.

María Pereda

The problem of free-riding arises when individuals benefit from a shared resource, service, or public good without contributing proportionately to its provision. This conduct often leads to a collective action problem, as individuals pursue personal gains while relying on the contributions of others. In this study, we present a Bayesian inference model to elucidate the behaviour of participants in a Public Goods Game, a conceptual framework that captures the essence of the free-riding problem. Here, individuals possess information on the distribution of group donations to the public good. Our model is grounded in the premise that individuals strive to harmonise their actions with the group's donation patterns. Our model is able to replicate behavioural patterns that resemble those observed in experiments with midsized groups (100 people), but fails to replicate those for larger scales (1000 people). Our results suggest that, in these scenarios, humans prefer imitation and convergence behaviours over profit optimisation. These insights contribute to understanding how cooperation is achieved through alignment with group behaviour.

Yubo Xiao, Muxi Lin, Lu Wang

Abstract To investigate the impact of Green Digital Finance (GDF) policies on sustainable regional development goals, this study exploits the implementation of China’s green finance reform and innovation pilot zones as a quasi-natural experiment to examine the theory and impact of policy channels on sustainable development. A difference-in-differences model was applied to evaluate the impact of policies in these zones based on data from 285 cities in China from 2014 to 2020. Research has shown that the GDF is conducive to achieving sustainable development goals through the effects of financial inclusion and energy transitions, which promote the transformation and upgrading of industrial structures. The impact of the GDF pilot-zone policies on the sustainable development of cities at different levels, locations, resource endowments, and green total factor productivity is heterogeneous. This study provides accurate empirical evidence of the effects of the extensive implementation of the policies adopted in the pilot zones and the expansion of the scale of these zones, and it provides policy recommendations for the GDF.

Juan Carlos Velasco Perdigones

Thiago Baldani Gomes De Filippo

Este artigo discorre sobre a ilegitimidade da contravenção penal de exploração de jogos de azar, à luz das teorias da proteção de bens jurídicos e do paternalismo penal, situados no contexto da proporcionalidade penal, concluindo que o tipo contravencional não foi recepcionado pela Constituição de 1988.

Wen-Jie Liu, Zi-Xian Li, Wen-Bo Li et al.

Recently, Sato et al. proposed an public verifiable blind quantum computation (BQC) protocol by inserting a third-party arbiter. However, it is not true public verifiable in a sense, because the arbiter is determined in advance and participates in the whole process. In this paper, a public verifiable protocol for measurement-only BQC is proposed. The fidelity between arbitrary states and the graph states of 2-colorable graphs is estimated by measuring the entanglement witnesses of the graph states,so as to verify the correctness of the prepared graph states. Compared with the previous protocol, our protocol is public verifiable in the true sense by allowing other random clients to execute the public verification. It also has greater advantages in the efficiency, where the number of local measurements is O(n^3*log {n}) and graph states' copies is O(n^2*log{n}).

Nina Rizun, Aleksandra Revina, Noella Edelmann

The public sector faces several challenges, such as a number of external and internal demands for change, citizens' dissatisfaction and frustration with public sector organizations, that need to be addressed. An alternative to the traditional top-down development of public services is co-creation of public services. Co-creation promotes collaboration between stakeholders with the aim to create better public services and achieve public values. At the same time, data analytics has been fuelled by the availability of immense amounts of textual data. Whilst both co-creation and TA have been used in the private sector, we study existing works on the application of Text Analytics (TA) techniques on text data to support public service co-creation. We systematically review 75 of the 979 papers that focus directly or indirectly on the application of TA in the context of public service development. In our review, we analyze the TA techniques, the public service they support, public value outcomes, and the co-creation phase they are used in. Our findings indicate that the TA implementation for co-creation is still in its early stages and thus still limited. Our research framework promotes the concept and stimulates the strengthening of the role of Text Analytics techniques to support public sector organisations and their use of co-creation process. From policy-makers' and public administration managers' standpoints, our findings and the proposed research framework can be used as a guideline in developing a strategy for the designing co-created and user-centred public services.