Hasil untuk "Labor market. Labor supply. Labor demand"

Naomi Fridman, Berta Ben Shachar

Intrapartum cardiotocography (CTG) is widely used for fetal monitoring during labor, yet its interpretation suffers from high inter-observer variability and limited predictive accuracy. Deep learning approaches have been constrained by the scarcity of CTG recordings with clinical outcome labels. We present the first application of self-supervised pre-training to intrapartum CTG analysis, leveraging 2,444 hours of unlabeled recordings for masked pre-training followed by fine-tuning on the 552-recording CTU-UHB benchmark. Using a PatchTST transformer architecture with a channel-asymmetric masking scheme designed for fetal heart rate reconstruction, we achieve an area under the receiver operating characteristic curve of 0.83 on the full test set and 0.853 on uncomplicated vaginal deliveries, exceeding previously reported results on this benchmark (0.68-0.75). Error analysis reveals that false-positive alerts typically correspond to CTG patterns judged concerning on retrospective clinical review, suggesting clinically meaningful predictions even when umbilical pH is normal. We release standardized dataset splits and model weights to enable reproducible benchmarking. Our results demonstrate that self-supervised pre-training can address data scarcity in fetal monitoring, offering a path toward reliable decision support in the labor room.

Yiru Liu, Zhen Xu

Abstract This paper conducts a meta-regression analysis of 319 estimates from 76 empirical studies that quantify the unexplained component of hukou-based relative wage gaps in China. On average, the reported unexplained relative hukou wage gap across studies is 10.6%. We investigate how methodological and data-related choices influence the heterogeneity in reported estimates. Our findings show that studies using administrative data tend to report smaller unexplained gaps, and estimates appear to depend on the dimension of hukou being considered, as studies focusing on the urban–rural hukou dimension produce larger estimates. Estimates derived from Oaxaca–Blinder–Kitagawa methods are significantly larger than those using simple dummy variable regressions. The use of monthly wages leads to an underestimation of the gap, and that omitting controls for health status and industry systematically biases estimates downward. Tests for publication bias reveal no systematic reporting bias but consistent evidence of a genuine positive effect, confirming a significant underlying unexplained hukou-based wage gap. Overall, this study highlights how empirical choices shape the evidence base on hukou-related inequality and underscores the structural nature of wage disparities in China’s labour market.

Jaume AGUSTÍ CULLELL, Jordi AGUSTÍ PANAREDA

Pour les auteurs, la fascination et la peur que suscite l’intelligence artificielle (IA) découlent d’une compréhension erronée de l’intelligence humaine (IH) qui, elle, peine à déployer son potentiel. L’enthousiasme qui entoure l’IA occulte souvent la déshumanisation concomitantede l’IH.  Considérant le travail comme un champ où se dessine le futur de l’intelligence, ils défendent une conception plus étendue de l’IH, rappelant ses différentes dimensions et capacités constitutives et les limites souvent oubliées de l’IA. Ils voient dans les multiples défis de notre époque charnière une chance inédite de cultiver au travail les dimensions intrinsèquement humaines de l’intelligence, indispensables à l’humanisation du travail, mais largement inexploitées.

Cella M. Sum, Anna Konvicka, Mona Wang et al.

The tech industry's shifting landscape and the growing precarity of its labor force have spurred unionization efforts among tech workers. These workers turn to collective action to improve their working conditions and to protest unethical practices within their workplaces. To better understand this movement, we interviewed 44 U.S.-based tech worker-organizers to examine their motivations, strategies, challenges, and future visions for labor organizing. These workers included engineers, product managers, customer support specialists, QA analysts, logistics workers, gig workers, and union staff organizers. Our findings reveal that, contrary to popular narratives of prestige and privilege within the tech industry, tech workers face fragmented and unstable work environments which contribute to their disempowerment and hinder their organizing efforts. Despite these difficulties, organizers are laying the groundwork for a more resilient tech worker movement through community building and expanding political consciousness. By situating these dynamics within broader structural and ideological forces, we identify ways for the CSCW community to build solidarity with tech workers who are materially transforming our field through their organizing efforts.

David Ellerman

Neoclassical economic theory presents marginal productivity (MP) theory using the scalar notion of marginal products, and takes pains, implicitly or explicitly, to show that competitive equilibrium satisfies the supposedly ethical principle: ``To each what he and the instruments he owns produces.'' This paper shows that MP theory can also be formulated in a mathematically equivalent way using vectorial marginal products--which however conflicts with the above-mentioned ``distributive shares'' picture. Vectorial MP theory also facilitates the presentation of modern treatment of the labor theory of property which on the descriptive side is based on the fact that, contrary to the distributive shares picture, one legal party gets the production vector consisting of 100 percent of the liabilities for the used-up inputs and 100 percent of the produced outputs in a productive opportunity. On the normative side, the labor theory of property is just the application of the usual juridical norm of imputation to the question of property appropriation. Keywords: marginal productivity theory, property theory, imputation of responsibility, vectorial marginal products JEL Classification]{D2, D3, D63, P14

Lei Chen, Chaoyue Gao, Alvin Leung et al.

Generative artificial intelligence (GenAI) like Large Language Model (LLM) is increasingly integrated into digital platforms to enhance information access, deliver personalized experiences, and improve matching efficiency. However, these algorithmic advancements rely heavily on large-scale user data, creating a fundamental tension between information assurance-the protection, integrity, and responsible use of privacy data-and artificial intelligence-the learning capacity and predictive accuracy of models. We examine this assurance-intelligence trade-off in the context of LinkedIn, leveraging a regulatory intervention that suspended the use of user data for model training in Hong Kong. Using large-scale employment and job posting data from Revelio Labs and a Difference-in-Differences design, we show that restricting data use significantly reduced GenAI efficiency, leading to lower matching rates, higher employee turnover, and heightened labor market frictions. These effects were especially pronounced for small and fast-growing firms that rely heavily on AI for talent acquisition. Our findings reveal the unintended efficiency costs of well-intentioned data governance and highlight that information assurance, while essential for trust, can undermine intelligence-driven efficiency when misaligned with AI system design. This study contributes to emerging research on AI governance and digital platform by theorizing data assurance as an institutional complement-and potential constraint-to GenAI efficacy in data-intensive environments.

Jeremy M. Wachter

I describe the assessment framework of labor-based contract grading (LBCG). In a labor-based grading scheme, the time and effort ("labor") a student spends on an assignment determines the credit they receive; the contract component requires students to design projects with clearly-defined goals and deliverables which must be satisfied to earn credit. LBCG is intended to promote student agency and engagement, and to provide a more equitable assessment framework given that students come with a wide range of prior experiences and preparation. I illustrate the LBCG framework within the context of an upper level physics course, using a particular assignment as an example; I also provide information on student experiences and engagement.

Yuval Rymon

AI is transforming human labor at an unprecedented pace - improving 10$\times$ per year in training effectiveness. This paper analyzes how society can adapt to AI-driven human-labor automation (HLA), using Bernardi et al.'s societal adaptation framework. Drawing on literature from general automation economics and recent AI developments, the paper develops a "threat model." The threat model is centered on mass unemployment and its socioeconomic consequences, and assumes a non-binary scenario between full AGI takeover and swift job creation. The analysis explores both "capability-modifying interventions" (CMIs) that shape how AI develops, and "adaptation interventions" (ADIs) that help society adjust. Key interventions analyzed include steering AI development toward human-complementing capabilities, implementing human-in-the-loop requirements, taxation of automation, comprehensive reorientation of education, and both material and social substitutes for work. While CMIs can slow the transition in the short-term, significant automation is inevitable. Long-term adaptation requires ADIs - from education reform to providing substitutes for both the income and psychological benefits of work. Success depends on upfront preparation through mechanisms like "if-then commitments", and crafting flexible and accurate regulation that avoids misspecification. This structured analysis of HLA interventions and their potential effects and challenges aims to guide holistic AI governance strategies for the AI economy.

Tarleton Gillespie, Ryland Shaw, Mary L. Gray et al.

As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. "Red-teaming" has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test their safety mechanisms and uncover vulnerabilities. Yet we know far too little about this work or its implications. This essay calls for collaboration between computer scientists and social scientists to study the sociotechnical systems surrounding AI technologies, including the work of red-teaming, to avoid repeating the mistakes of the recent past. We highlight the importance of understanding the values and assumptions behind red-teaming, the labor arrangements involved, and the psychological impacts on red-teamers, drawing insights from the lessons learned around the work of content moderation.

Henrique Tahan Novaes, Flávio Chedid Henriques, Bruna Oliveira Martins

Este texto tem o objetivo de apresentar a estrutura e os principais debates da 1a Escola Internacional de Autogestão, realizada na Escola Nacional Florestan Fernandes (ENFF) entre os dias 19 e 23 de abril de 2023. A fim de aprofundar os debates ocorridos nos encontros realizados pela Rede “Economia dos/as Trabalhadores/as”, vivenciamos com trabalhadores de cooperativas, membros do poder público, estudantes e pesquisadores universitários dilemas do trabalho e pesquisa no campo da autogestão e tensionamentos existentes entre o mundo do trabalho e a academia, que tentaremos abordar neste ensaio. Palavras-chave: Autogestão; Economia Solidária; Empresas Recuperadas por Trabalhadores; Escola Nacional Florestan Fernandes; Pedagogia da Autogestão.

darciel da costa, Suely Aparecida Martins

Analisamos, neste artigo, experiências de jovens, egressos do Ensino Médio e residentes no Conjunto Habitacional Terra Nossa, em Francisco Beltrão - PR, a respeito da escola e do emprego. Com base no materialismo histórico-dialético, em documentos e entrevistas semiestruturadas com oito jovens, identificamos que coexistem duas redes de ensino voltadas ao enquadramento das juventudes aos postos de trabalho, tendo em conta etnia, classe social, renda e gênero. A rede voltada aos trabalhadores visa à formação para o mercado de trabalho. Concluímos que a efetiva vivência da moratória social é limitada pela condição de classe. Palavras-chave: Trabalho; Educação; Juventude; Desigualdade Social; Escola.

Valery A. Gurtov, Evgeny A. Pitoukhin, Mikhail Y. Nasadkin

In this paper, we consider an approach to evaluating the HEI effectiveness in terms of graduate employment. Data source – the monitoring of graduates employment conducted by the Ministry of Education of Russian Federation. It is proposed to calculate the efficiency of graduates employment in view of the situation on the labor market expressed with an imbalance of supply and demand.

Marco Antonio Jorge

The present paper aims to model the Wage Compensating Differentials Theory, expliciting its occurrence conditions. So, the first section presents wage hedonic theory to show market clearing in the presence of differences in agents’ preferences and production tecnologies. It is possible to verify that matching between workers and firms is not randomic and even competitive markets show wage differentials. Following, it analyses the differential sign, noting that this could be negative in the case of expressive risk-lover workers supply simultaneously with a restrict demand for such workers. Lastly, in the third section the consequences of public intervention in the labor market are discussed and it is showed that this could be benefic for the workers in front of assimetric information situations, where they own mistaken perception about the risks they face in their workplaces.

Lia Tiriba, José Luiz Cordeiro Antunes, Jacqueline Aline Botelho Lima et al.

Conforme a caracterização precisa de Arantes (2016), a história do tempo presente é encapsulada em um regime de espera destituído de perspectivas para o futuro. O novo tempo do mundo, encharcado de presentismo, parece acelerar-se através do livre fluir do capital e do desenrolar de eventos apocalípticos em cadeia, enquanto o próprio sentido de história vai se esvaindo.

José Pedro Pinto

Aaron Paul M. Dela Rosa

This study was developed to describe the daily operations and encountered problems of the National Labor Relations Commission Regional Arbitration Branch No. IV (NLRC RAB IV) through conducted observations and interviews. These problems were addressed and analyzed to be the features of the developed web-based management information system (MIS) for cases. The research methodology utilized in this project was the descriptive developmental approach. The Agile Software Development methodology was followed to develop the system. It was used to quickly produce the desired output while allowing the user to go back through phases without finishing the whole cycle. The system covered managing filed complaints, Single-Entry Approach (SEnA), labor cases, and report generation. The findings, through the interview, of handling records were inconsistent and inaccurate. This study also focused on ensuring the Data Privacy Act of 2012, protecting the database's information using the XOR Cipher Algorithm. This study was evaluated using standard web evaluation criteria. Using the criteria, the study's overall mean was 4.27 and 4.43, with the descriptive meaning of Very Good, which showed that the system was accepted as perceived by experts and end-users, respectively. Management of filed cases is a vital process for the Commission. With that said, developing a web-based management information system could ease the internal operations of handling and managing filed labor cases. Moreover, respondents and complainants can easily determine their filed cases' status using the case status tracking system. For further improvements to the system, additional printable documents may be added that could be found needed by the Commission. Lastly, further research about the effectiveness of the web-based system may be conducted for further enhancements of the system.

Ruyuan Wan, Naome Etori, Karla Badillo-Urquiola et al.

The bridging research between Human-Computer Interaction and Natural Language Processing is developing quickly these years. However, there is still a lack of formative guidelines to understand the human-machine interaction in the NLP loop. When researchers crossing the two fields talk about humans, they may imply a user or labor. Regarding a human as a user, the human is in control, and the machine is used as a tool to achieve the human's goals. Considering a human as a laborer, the machine is in control, and the human is used as a resource to achieve the machine's goals. Through a systematic literature review and thematic analysis, we present an interaction framework for understanding human-machine relationships in NLP. In the framework, we propose four types of human-machine interactions: Human-Teacher and Machine-Learner, Machine-Leading, Human-Leading, and Human-Machine Collaborators. Our analysis shows that the type of interaction is not fixed but can change across tasks as the relationship between the human and the machine develops. We also discuss the implications of this framework for the future of NLP and human-machine relationships.

Ashwin Singh, Arvindh Arun, Ayushi Jain et al.

Google Play's policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. Finally, we conclude with a discussion on how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play.

Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda et al.

Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods. Using tabulated summaries of incomes from tax authorities and a recent estimation method, we estimate income Pareto exponents in U.S. for 1916-2019. We find that during the past three decades, the capital and labor income Pareto exponents have been stable at around 1.2 and 2. Our findings suggest that the top tail income and wealth inequality is higher and wealthy agents have twice as large an impact on the aggregate economy than previously thought but there is no clear trend post-1985.

Inkyu Shin, Dong-jin Kim, Jae Won Cho et al.

Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despite these efforts, UDA still has a long way to go to reach the fully supervised performance. To this end, we propose a Labeling Only if Required strategy, LabOR, where we introduce a human-in-the-loop approach to adaptively give scarce labels to points that a UDA model is uncertain about. In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2.2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)". To further reduce the efforts of the human annotator, we also propose "Point-based Pixel-Labeling (PPL)", which finds the most representative points for labeling within the generated inconsistency mask. This reduces efforts from 2.2% segment label to 40 points label while minimizing performance degradation. Through extensive experimentation, we show the advantages of this new framework for domain adaptive semantic segmentation while minimizing human labor costs.