Hasil untuk "Labor policy. Labor and the state"

Guanghe Han, Jiahui Xu, Xin Zhang et al.

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Koichi Tanaka, Kazuki Kawamura, Takanori Muroi et al.

Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic. In this paper, we propose novel estimators, Click-based Inverse Propensity Score (CIPS), exploiting the intrinsic stochasticity of user click behavior to address this challenge. Unlike existing methods that rely on the stochasticity of the logging policy, our approach uses click probability as a new form of importance weighting, enabling low-bias OPE even under deterministic logging policies where existing methods incur substantial bias. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias compared to strong baselines, for a range of experimental settings with completely deterministic logging policies.

Linxuan Xia, Xiaolong Yang, Yongyuan Chen et al.

Aligning large language models (LLMs) on domain-specific data remains a fundamental challenge. Supervised fine-tuning (SFT) offers a straightforward way to inject domain knowledge but often degrades the model's generality. In contrast, on-policy reinforcement learning (RL) preserves generality but fails to effectively assimilate hard samples that exceed the model's current reasoning level. Recent off-policy RL attempts improve hard sample utilization, yet they suffer from severe training instability due to the forced distribution shift toward off-policy knowledge. To reconcile effective off-policy knowledge absorption with the stability of on-policy RL, we propose Rephrasing Policy Optimization (RePO). In RePO, the policy model is prompted to first comprehend off-policy knowledge and then rephrase it into trajectories that conform to its own stylistic and parametric distribution. RePO dynamically replaces low-reward rollouts with these rephrased, high-quality trajectories. This strategy guides the model toward correct reasoning paths while strictly preserving on-policy training dynamics. Experiments on several benchmarks demonstrate that RePO improves hard-sample utilization and outperforms existing baselines, achieving state-of-the-art performance.

Hanyong Wang, Menglong Yang

Deep reinforcement learning has been able to solve various tasks successfully, however, due to the construction of policy gradient and training dynamics, tuning deep reinforcement learning models remains challenging. As one of the most successful deep reinforcement-learning algorithm, the Proximal Policy Optimization algorithm (PPO) clips the policy gradient within a conservative on-policy updates, which ensures reliable and stable policy improvement. However, this training pattern may sacrifice sample efficiency. On the other hand, off-policy methods make more adequate use of data through sample reuse, though at the cost of increased the estimation variance and bias. To leverage the advantages of both, in this paper, we propose a new PPO variant based on the stability guarantee from conservative on-policy iteration with a more efficient off-policy data utilization. Specifically, we first derive an extended off-policy improvement from an expectation form of generalized policy improvement lower bound. Then, we extend the clipping mechanism with segmented exponential functions for a suitable surrogate objective function. Third, the trajectories generated by the past $M$ policies are organized in the replay buffer for off-policy training. We refer to this method as Extended Off-policy Proximal Policy Optimization (ExO-PPO). Compared with PPO and some other state-of-the-art variants, we demonstrate an improved performance of ExO-PPO with balanced sample efficiency and stability on varied tasks in the empirical experiments.

Heather CONNOLLY

Počuč Pavle

Sensitive and vulnerable groups in society must be provided with equal employment and social status with great empathy and social responsibility, as they are characterized by a strong desire to contribute to the community in which they live. In addition to the economic benefits that enable financial independence, persons with disabilities who are employed regain a sense of personal value, dignity, and equal status in society. The fact that they contribute to societal development through their work further motivates them and strengthens their sense of belonging to that society. Most persons with disabilities are marked by exceptional dedication, responsibility, discipline, and motivation; however, unfortunately, they are often unjustly neglected and discriminated against by employers. Through synchronized action and engagement of all three sectors, it is possible to significantly improve the position of persons with disabilities in the labor market and directly influence the strengthening of the country's socio-economic status. By creating a clear strategy of assistance and support for employment led by the state, especially through concrete active employment policy measures, a noticeable trend in reducing their unemployment rates can be observed. The aim of this paper is to present the employment measures for hard-to-employ categories, with an emphasis on persons with disabilities, as well as to analyze the impact of these measures on the employment status and socio-economic position of persons with disabilities. Furthermore, the paper will present survey results along with future directions and recommendations.

Lujun Li, Lama Sleem, Niccolo' Gentile et al.

The sampling temperature, a critical hyperparameter in large language models (LLMs), modifies the logits before the softmax layer, thereby reshaping the distribution of output tokens. Recent studies have challenged the Stochastic Parrots analogy by demonstrating that LLMs are capable of understanding semantics rather than merely memorizing data and that randomness, modulated by sampling temperature, plays a crucial role in model inference. In this study, we systematically evaluated the impact of temperature in the range of 0 to 2 on data sets designed to assess six different capabilities, conducting statistical analyses on open source models of three different sizes: small (1B--4B), medium (6B--13B), and large (40B--80B). Our findings reveal distinct skill-specific effects of temperature on model performance, highlighting the complexity of optimal temperature selection in practical applications. To address this challenge, we propose a BERT-based temperature selector that takes advantage of these observed effects to identify the optimal temperature for a given prompt. We demonstrate that this approach can significantly improve the performance of small and medium models in the SuperGLUE datasets. Furthermore, our study extends to FP16 precision inference, revealing that temperature effects are consistent with those observed in 4-bit quantized models. By evaluating temperature effects up to 4.0 in three quantized models, we find that the Mutation Temperature -- the point at which significant performance changes occur -- increases with model size.

Weizhen Wang, Jianping He, Xiaoming Duan

Policy gradient methods are one of the most successful approaches for solving challenging reinforcement learning problems. Despite their empirical successes, many state-of-the-art policy gradient algorithms for discounted problems deviate from the theoretical policy gradient theorem due to the existence of a distribution mismatch. In this work, we analyze the impact of this mismatch on policy gradient methods. Specifically, we first show that in the case of tabular parameterizations, the biased gradient induced by the mismatch still yields a valid first-order characterization of global optimality. Then, we extend this analysis to more general parameterizations by deriving explicit bounds on both the state distribution mismatch and the resulting gradient mismatch in episodic and continuing MDPs, which are shown to vanish at least linearly as the discount factor approaches one. Building on these bounds, we further establish guarantees for the biased policy gradient iterates, showing that they approach approximate stationary points with respect to the exact gradient, with asymptotic residuals depending on the discount factor. Our findings offer insights into the robustness of policy gradient methods as well as the gap between theoretical foundations and practical implementations.

Imene Kolli, Ario Saeid Vaghefi, Chiara Colesanti Senni et al.

InfluenceMap's LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity's support or opposition to science-based policy pathways for achieving the Paris Agreement's goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.

Antonio Fernández García

Con el título “El derecho a la salud y la Seguridad Social” se presenta la primera monografía de Mónica Ricou Casal, incorporada en febrero de 2024 a la Universitat Oberta de Catalunya (UOC) como Profesora Lectora de Derecho del Trabajo y de la Seguridad Social. Doctora por la Universitat de Girona tras defender la tesis “La transversabilidad normativa de la salud como derecho” (2023), sus líneas de investigación se han centrado hasta la fecha en la Seguridad Social y la protección a la salud bajo la perspectiva de género. Pese a esta corta carrera como investigadora, la autora ya ha obtenido un premio1 y atesora gran experiencia profesional en el ámbito del Derecho del Trabajo.

Merve Çiloğlu Yörübulut

Family is an ancient institution that provides humanity shelter from birth to death. The family institution, which has been affected by the fractures throughout history, has continued to exist by adapting. However, this adaptation process has also brought about changes in the structure of families. In postmodern societies, the family, like many other phenomena, faces many threats while being rebuilt on an ambiguous basis. Among the threats to by the family, the central position of the concept of work in social functioning is important. The concept of work lies at the centre of postmodern societies and is critically important. In this manner, where people primarily express themselves through their positions in working life, with technological developments, they become mentally integrated even though they are physically outside the work area. Protecting the family institution, which is an actor in social policy, is only possible by protecting it in working life. The most effective policies that can protect the family during working life can be produced through labour law regulations, which are also an important tool in social policy. This study highlights the need to protect the family in working life, examines the regulations that protect the family in labour law, identifies the issues that need to be protected, and offers suggestions for more effective protection.

Yismaw Ayelign Mengistu, Lakhwinder Singh

Abstract The objective of this study is to examine the performance of the manufacturing firms in terms of productivity growth by accounting for the determining factors. The findings revealed that growth in TFP has an average rate of 4.87% per year and showed significant improvement over the three policy periods (1996–2003, 2004–2010, and 2011–2015), respectively 3.6%, 4.7%, and 6.5%. Total factor productivity has been decomposed into contributing sources of technical progress (1%), scale change (− 0.14%), and technical efficiency change (4.01%). Twelve factors are identified from the literature as determinants of TFP growth. Eight of them are found to statistically affect this growth. The periods from 2004 to 2010 (high economic growth period) and from 2011 to 2015 (GTP I) have a higher TFP growth rate relative to the base period (1996–2003) and similar private ownership. In addition, labor input growth, capital intensity, and growth in average wage contributed positively to TFP growth. Importers of raw material inputs have higher TFP growth rates compared with non-importers. Tone can conclude from this finding that Ethiopian large and medium-scale manufacturing firms are low in terms of TFP growth. There are factors that enhance productivity, and there are also others that hinder improvements. Thus, strategically designed, coordinated with the firms themselves, and focused interventions are required in order to raise the real sales and value added using the existing state-of-the art technology and input combination, along with advancing the technical know-how, improving labor quality, updating, and renovating the capital input.

F. Rojas, Xuan Jiang, Laura Montenovo et al.

The relationship between population health and measures of economic well-being and economic activity is a long standing topic in health economics (Preston, 1975; Cutler, Deaton, and Lleras-Muney, 2006; Ruhm, 2000). The conceptual issues in analyzing the complicated link between health and economic well-being are central to understanding the implications of the COVID-19 epidemic in the United States The public health shock of the epidemic has direct economic impacts, but the mitigation policies governments are using to control the spread of the virus may also damage economic activity. We estimate how state job market conditions respond to state COVID-19 infections and school closures, which are the earliest of the major mitigation policies. Mitigation policies and local epidemiological conditions explain some of the variation in unemployment patterns. However, the historically unprecedented increase in new UI claims during the weeks of March 15-21 and March 22-28 was largely across-the-board and occurred in all states. This suggests most of the economic disruption was driven by the health shock itself. Put differently, it appears that the labor market slowdown was due primarily to a nationwide response to evolving epidemiological conditions and that individual state policies and own epidemiologic situations have had a comparatively modest effect.

Pedro Rossi

Transcrição da conferência do Prof. Dr. Pedro Rossi na mesa de abertura do V INTERCRÍTICA (10 e 11 de outubro de 2022) que foi realizado nas dependências da Escola Politécnica de Saúde Joaquim Venâncio da Fundação Oswaldo Cruz (EPSJV/Fiocruz) e da Universidade Estadual do Rio de Janeiro (UERJ).

Daniela Inclezan

This paper introduces a framework for assisting policy authors in refining and improving their policies. In particular, we focus on authorization and obligation policies that can be encoded in Gelfond and Lobo's AOPL language for policy specification. We propose a framework that detects the statements that make a policy inconsistent, underspecified, or ambiguous with respect to an action being executed in a given state. We also give attention to issues that arise at the intersection of authorization and obligation policies, for instance when the policy requires an unauthorized action to be executed. The framework is encoded in Answer Set Programming. Under consideration for acceptance in TPLP.

A. Vankevich, Iryna N. Kalinovskaya

The purpose of the study is to determine the directions of formation of a digital ecosystem of the labor market in the Republic of Belarus and to substantiate the methodological issues of its functioning. The essence and main elements of the digital ecosystem of the labor market have been defined. The difficulties in the formation of a unified information database for labor market analytics (different criteria for structuring information in the description of vacancies and resumes; the lack of a unified skills classifier; the closedness of information) have been established. The stages of formation of a digital ecosystem of the labor market and the directions of its normative and methodological support have been developed. The methodology of evaluation of online sources of data on the labor market in the Republic of Belarus has been developed. As a result of approbation of this methodology, the most significant indicators necessary to be included in the form of description of vacancies and resumes to ensure their uniformity on different online sources and the possibility of comparison have been established, as well as the most informative and reliable online sources of information on the labor market, the information of which should be included in the emerging digital ecosystem of the labor market, have been determined. Combining information from various sources will allow to expand the knowledge base on the state of the labor market, obtain additional results of its analysis, make more reasonable conclusions, which will significantly affect the effectiveness of decisions in the field of employment policy

W. Dow, A. Godøy, C. Lowenstein et al.

Do minimum wages and the earned income tax credit (EITC) mitigate rising "deaths of despair?" We leverage state variation in these policies over time to estimate event study and difference-in-differences models of deaths due to drug overdose, suicide, and alcohol-related causes. Our causal models find no significant effects on drug or alcohol-related mortality, but do find significant reductions in non-drug suicides. A 10 percent minimum wage increase reduces non-drug suicides among low-educated adults by 2.7 percent, and the comparable EITC figure is 3.0 percent. Placebo tests and event-study models support our causal research design. Increasing both policies by 10 percent would likely prevent a combined total of more than 700 suicides each year.

Bryan L. Sykes, M. Ballard, Daniela Kaiser et al.

Obtaining employment is a major barrier to social reintegration for people on probation or parole. Research on the reentry process identifies several mechanisms that accentuate difficulties in locating work, including human capital development, structural changes in the labor market, and onerous probation and parole conditions. In this article, we review theories that explain low labor market participation rates among people reentering society, and we draw on multiple sources of data to identify the types of jobs that are available to people with low human capital. We find that nearly a quarter of people in America’s state and federal prisons had permanently removed themselves from the formal labor market before their most recent arrest; however, exclusionary hiring practices in the formal labor market often push those carrying the stigma of a criminal record into underground or informal labor markets, where wage rates are markedly higher than the federal minimum wage. Our findings demonstrate that severe and chronic employment struggles often predate and follow incarceration. We provide a detailed discussion of policy reform proposals that could help to remedy this harmful dynamic.

Katrin Menke, Andrea Rumpel

Concepts such as “belonging” (Yuval‐Davis, 2011) and “community of value” (Anderson, 2013) try to capture the multiple ways of classifying migrants. In this article, we argue that belonging needs to be analyzed against the backdrop of active social citizenship in European welfare states. Although the literature acknowledges the increasing links between migration and social policies, the latest “turn to activation” in social policy has hardly been accounted for. By focusing on two policy fields in Germany, the labor market and health policies, we briefly describe discourses and social right entitlements and their ambivalences. Empirically we show (a) how bureaucrats within the two policy fields regulate and justify refugees’ social rights in practice and (b) how refugees act vis‐à‐vis relevant institutional opportunity structures. Our study contributes to previous research twofold: Firstly, we illustrate processes of positioning and selecting refugees that stem from recent social policy architecture. Secondly, we demonstrate everyday experiences from refugees’ vis‐á‐vis relevant institutional opportunity structures in Germany. Our results show that inconsistencies within and between social policy fields of one welfare state have to be taken into consideration for further national and transnational research.

Amanda Moreira da Silva

O livro de José Rodrigues, intitulado Os empresários e a educação superior, é pequeno na forma, mas grandioso no propósito e no conteúdo. Resgatar essa obra 15 anos depois de publicada é tarefa fundamental para todos aqueles que se propõem a entender os desafios da educação brasileira. Um estudo importante de ser retomado nos dias de hoje, quando novas dinâmicas de mercantilização e financeirização se inserem na educação superior e aprofundam o caráter privatizante das reformas em curso há mais de cinquenta anos no Brasil. O estatuto de clássico e ao mesmo tempo a atualidade do referido livro pode ser verificado pela riqueza de definições, princípios e conceitos apresentados e que o autor busca difundir ao longo de sua trajetória pujante no campo educacional, com contribuições importantíssimas para as reflexões acerca do empresariamento da educação. Palavras-chave: Empresariamento da educação. Educação superior. Mercantilização da educação.