搜索结果：government

Ensuring digital accessibility is essential for inclusive access to online services. However, many government and non-government websites that provide critical services - such as education, healthcare, and public administration - continue to exhibit significant accessibility and usability barriers. This study evaluates the accessibility of Bangladeshi government and non-government websites under WCAG~2.2 by combining automated accessibility assessments with user-reported feedback. A total of 212 websites were analyzed using multiple automated tools, complemented by a survey of 103 users to capture real-world usability, accessibility, and security experiences. The results reveal substantial disparities between government and non-government websites, highlighting persistent issues related to navigation complexity, interaction cost, visual readability, accessibility feature adoption, and authentication mechanisms. While non-government websites generally demonstrate better usability and functional performance, accessibility support remains inconsistent across both categories. The findings underscore the need for regular accessibility audits, user-centered design practices, and policy-d

Scholars and policymakers have vigorously debated what the impact of government spending on economic growth is. Some current research and theoretical models suggest that the reaction of economic growth to the extension of government spending can be either positive or negative. This article intends to investigate the inverted-U shaped relationship between output growth and government spending (i.e., government fixed capital formation [GFCF] and government final consumption expenditure [GFCE]). Ordinary least squares (OLS) is employed as an approach to annual data for Cambodia obtained from 1971 to 2015. The result reveals that GFCF and GFCE have an inverted-U shaped relation with economic growth and that 5.40% and 7.23% are the optimal values of GFCF and GFCE, respectively. The labour growth rate and export growth rate contribute positively to the growth rate of output. This study indicates that the increasing level of government expenditure reduces the efficacy of government spending, and also helps Cambodia's policymakers to control fiscal policy more efficiently.

The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues persist. Rather than auditing real-world deployed systems, our goal is to understand why widely adopted bias mitigation techniques often fail when applied to government data. Our findings reveal that bias mitigation approaches applied to government data are not always effective -- not because of flaws in model architecture or metric selection, but due to the inherent properties of the data itself. Through comparing a set of comprehensive models and fairness methods, our experiments consistently show that the mitigation efforts cannot overcome the embedded unfairness in the data -- further reinforcing that the origin of bias lies in the structure and history of government datasets. We then explore the reasons for the mitigation failures in predictive models on government data and highlight the potential sources of unfairness posed by data distribution shifts, the accumulation of historical bias, and delays in data release. We also discover the limitations of the blind

We propose a new approach to estimate government worker skills, a setting where output is hard to observe and wages may be uninformative about skills. The approach uses wages in comparable jobs in the private sector and machine learning tools to link skills to skill-related observables. We apply the approach to rich Indonesian household-level panel data from 1988-2014, showing two main applications. First, government skills have continuously declined relative to the private sector, driven by the most skilled workers ending up in the private sector. Second, the Indonesian government pays a wage premium of 43% conditional on skills.

Governments typically collect and steward a vast amount of high-quality data on their citizens and institutions, and the UK government is exploring how it can better publish and provision this data to the benefit of the AI landscape. However, the compositions of generative AI training corpora remain closely guarded secrets, making the planning of data sharing initiatives difficult. To address this, we devise two methods to assess UK government data usage for the training of Large Language Models (LLMs) and 'peek behind the curtain' in order to observe the UK government's current contributions as a data provider for AI. The first method, an ablation study that utilises LLM 'unlearning', seeks to examine the importance of the information held on UK government websites for LLMs and their performance in citizen query tasks. The second method, an information leakage study, seeks to ascertain whether LLMs are aware of the information held in the datasets published on the UK government's open data initiative data$.$gov$.$uk. Our findings indicate that UK government websites are important data sources for AI (heterogenously across subject matters) while data$.$gov$.$uk is not. This paper s

Governments increasingly deploy AI in public services, making transparency essential for accountability and public trust. Australia's Standard for AI Transparency Statements (AITS) requires government bodies to disclose how AI is used in practice, yet little empirical evidence exists on how these requirements are realised in documents. This paper presents the first government AITS dataset, dubbed AITS-101, and provides the first systematic analysis of their content. Using stylometric, quantitative, and qualitative document analyses, we examine disclosure coverage, structure, and recurring patterns. Our findings reveal substantial variation in AI-related practice disclosure, highlight gaps between policy intent and implementation, and inform the design of more effective public-sector AI transparency standards.

Current evaluations of LLMs in the government domain primarily focus on safety considerations in specific scenarios, while the assessment of the models' own core capabilities, particularly domain relevance, remains insufficient. To address this gap, we propose GovRelBench, a benchmark specifically designed for evaluating the core capabilities of LLMs in the government domain. GovRelBench consists of government domain prompts and a dedicated evaluation tool, GovRelBERT. During the training process of GovRelBERT, we introduce the SoftGovScore method: this method trains a model based on the ModernBERT architecture by converting hard labels to soft scores, enabling it to accurately compute the text's government domain relevance score. This work aims to enhance the capability evaluation framework for large models in the government domain, providing an effective tool for relevant research and practice. Our code and dataset are available at https://github.com/pan-xi/GovRelBench.

Understanding where Internet services are hosted, and how users reach them, has captured the interest of government regulators and others concerned with the privacy of data flows. In this paper we focus on government websites -- services which arguably merit a higher expectation of protection against foreign surveillance or interference -- and seek to identify countries in the middle (CitMs): countries that are neither the source nor destination in a path for a resident visiting their online government services. Finding these CitMs raises daunting methodological challenges. We propose a framework to identify CitMs and use a pilot study of 149 countries to refine our methodology before conducting an in-depth measurement study of 11 countries. For our focused study, we compile an extensive set of websites hosting government services and analyze over 9,000 IP-level paths from vantage points in those countries to these services. We conduct extensive manual validation to corroborate or discard paths based on the aforementioned challenges, and discuss paths that experience unexpected CitMs.

This article focuses on the legal issues associated with open government data licenses. This study compares current open data licenses and argues that licensing terms reflect policy considerations, which are quite different from those contemplated in business transactions or shared in typical commons communities. This article investigates the ambiguous legal status of data together with the new wave of open government data, which concerns some fundamental intellectual property (IP) questions not covered by, or analyzed in depth in, the current literature. Moreover, this study suggests that government should choose or adapt open data licenses according to their own IP regimes. In the end, this article argues that the design or choice of open government data license forms an important element of information policy; government, therefore, should make this decision in accordance with their policy goals and in compliance with their own jurisdictions' IP laws.

Government development projects vary significantly from private sector initiatives in scope, stakeholder complexity, and regulatory requirements. There is a lack of empirical studies focusing on requirements engineering (RE) activities specifically for government projects. We addressed this gap by conducting a series of semi-structured interviews with 12 professional software practitioners working on government projects. These interviewees are employed by two types of companies, each serving different government departments. Our findings uncover differences in the requirements elicitation phase between government projects, particularly for data visualization aspects, and other software projects, such as stakeholders and policy requirements. Additionally, we explore the coverage of human and social aspects in requirements elicitation, finding that culture, team dynamics, and policy implications are critical considerations. Our findings also pinpoint the main challenges encountered during the requirements elicitation phase for government projects. Our findings highlight future research work that is important to bridge the gap in RE activities for government software projects.

This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models.In particular, we explore how BERT encodes the government relation between constituents in a sentence. We use several probing classifiers, and data from two morphologically rich languages. Our experiments show that information about government is encoded across all transformer layers, but predominantly in the early layers of the model. We find that, for both languages, a small number of attention heads encode enough information about the government relations to enable us to train a classifier capable of discovering new, previously unknown types of government, never seen in the training data. Currently, data is lacking for the research community working on grammatical constructions, and government in particular. We release the Government Bank -- a dataset defining the government relations for thousands of lemmas in the languages in our experiments.

With the rapid development of artificial intelligence and breakthroughs in machine learning and natural language processing, intelligent question-answering robots have become widely used in government affairs. This paper conducts a horizontal comparison between Guangdong Province's government chatbots, ChatGPT, and Wenxin Ernie, two large language models, to analyze the strengths and weaknesses of existing government chatbots and AIGC technology. The study finds significant differences between government chatbots and large language models. China's government chatbots are still in an exploratory stage and have a gap to close to achieve "intelligence." To explore the future direction of government chatbots more deeply, this research proposes targeted optimization paths to help generative AI be effectively applied in government chatbot conversations.

The issue of local government debt is widely recognized as one of the "gray rhinos" affecting the stable development of China's economy. Government debt can transmit risks to local banks, which are among the primary holders of local debt, thereby triggering systemic financial risks. Consequently, exploring debt resolution pathways and evaluating the systematic effects of debt servicing policies has become critically important. This study employs panel data from 348 local commercial banks across 29 provincial-level administrative regions in China from 2010 to 2023, and constructs a difference-in-differences (DID) model to investigate the impact of the State Council's special supervision of debt servicing on local bank risks. The findings indicate that the government's debt servicing policy essentially represents a shift of government debt from explicit to implicit forms, significantly increasing the risks faced by local banks and producing outcomes contrary to the policy's original intent. This effect is particularly pronounced for rural commercial banks and banks with high customer concentration and fewer branches. Mechanism analysis reveals two key insights. First, local banks are

Governments all around the world are widely investing on the implementation of e government to advance services to citizens and minimize costs. Governments can progress effectiveness of their operations and can carry their administrative operations efficiently with the help of ICT. Electronic government perceived to provide a way for governments to renovate their operational activities to serve their clients more competently. With improvement in Information and Communication Technology ICT it is now time to device electronic access to government facilities to the variously located citizens. E governments all around the world have different objectives and follow different models for e government development. Present models examined and found less than satisfactory to guide e-government implementation. This research proposed a hybrid model from Citizen comprehensive vision acknowledged the civic idea and The strategic framework of e-government models. The procedure of merging different computational knowledge systems to assemble a solitary crossover show has turned out to be progressively prominent. The execution files of these mixture models have ended up being superior to the indiv

We study optimal taxation when citizens hold beliefs about an honest versus opportunistic government and update those beliefs from observed taxes and delivery. In a Ramsey economy with competitive firms, the government privately knows its type: the honest type implements announced taxes and converts revenue into public goods, while the opportunistic type can strategically mimic or divert. Bayesian learning from policy choices and a noisy delivery signal disciplines taxation. We establish a trust cutoff: below it, optimal revenue is zero; above it, the revenue scale is increasing in reputation, with the dynamic cutoff lower than the static one. With broad instruments and symmetric monitoring, dynamic forces act through total revenue while the tax mix is indeterminate along a static equivalence frontier. More informative monitoring (in the Blackwell sense) expands fiscal scale and shrinks the no-tax region.

Globally, the discourse of e-government has gathered momentum in public service delivery. No country has been left untouched in the implementation of e-government. Several government departments and agencies are now using information and communication technology (ICTs) to deliver government services and information to citizens, other government departments, and businesses. However, most of the government departments have not provided all of their services electronically or at least the most important ones. Thus, this creates a phenomenon of e-government service gaps. The objective of this study was to investigate the contextual factors enhancing e-government service gaps in a developing country. To achieve this aim, the TOE framework was employed together with a qualitative case study to guide data collection and analysis. The data was collected through semi-structured interviews from government employees who are involved in the implementation of e-government services in Zimbabwe as well as from citizens and businesses. Eleven (11) factors were identified and grouped under the TOE framework. This research contributes significantly to the implementation and utilisation of e-governme

How can governments attract entrepreneurs and their businesses? The view that new business creation grows with the optimal level of government investments remains appealing to policymakers. In contrast with this active approach, we build a model where governments may adopt a passive approach to stimulating business creation. The insights from this model suggest new business creation depends positively on factors beyond government investments--attracting high-skilled migrants to the region and lower property prices, taxes, and fines on firms in the informal sector. These findings suggest whether entrepreneurs generate business creation in the region does not only depend on government investments. It also depends on location and skilled migration. Our model also provides methodological implications--the relationship between government investments and new business creation is endogenously determined, so unless adjustments are made, econometric estimates will be biased and inconsistent. We conclude with policy and managerial implications.

Does government transparency affect innovation? I evaluate the launch of a government database with detailed technical information on the universe of wireless-enabled products on the U.S. market (N 347 thousand). The results show the launch approximately doubled the use of new technologies in the following ten years, an indicator of follow-on innovation. The increase affected both products in the same and new product classes, suggesting novelty; waned over several years, potentially due to an increase in secrecy and patenting; and boosted foreign more than U.S. domestic competitors. These results highlight the importance of information for private sector innovation.

This study integrates critical AI scholarship with relational communication theories to explain how AI language modifications shape the quality of government-citizen communication. Distinguishing between informational-cognitive quality (clarity, ease of response) and expressive-constitutive quality (politeness, respectfulness, feeling heard, trust, urgency, empathy), we hypothesize that AI yields uncontested benefits for the former but contested effects for the latter, potentially enhancing relational markers while muting authentic emotional cues. Using a vignette-based survey with 220 citizens and 214 civil servants in China, we assess perceptions across five interaction contexts: service requests, policy inquiries, complaints, suggestions, and emergencies. Results from paired t-tests and mixed-effects regressions support the claim that AI enhances both informational-cognitive and expressive-constitutive quality from the perspectives of citizens and civil servants, with significant improvements in clarity, politeness, satisfaction, trust, and empathy, but provide no consistent evidence of urgency or empathy signals. These findings suggest that concerns over algorithmic emotional f