《易经》研究与吉狄马加诗歌翻译——美国翻译家梅丹理先生访谈录

论文针对吉狄马加诗歌翻译研究中遇到的一些问题,采访了当代著名汉学家、诗歌翻译家、诗人梅丹理先生。梅丹理先生重点阐释了《易经》研究对其解读诗歌意象的重要启示,并围绕吉狄马加其人其诗、诗歌音乐性、个人民族诗歌翻译方法、成果,以及民族诗学研究的重要意义和自己未来的研究计划等问题表达了自己的观点。这些真知灼见不仅为吉狄马加诗歌翻译个案研究提供了宝贵的资料,同时也为长期受冷落的当代民族诗歌翻译与研究带来了信心和希望。     

Does the Representation of Household Behavior Matter for Welfare Analysis of Tax-benefit Policies? An Introduction

A widely shared intuition holds that individual control over money matters for the decision process within the household and the subsequent distribution of resources and welfare. As a consequence, there are good reasons to depart from the unitary model of the household and to explore the possibilities offered by models of the family accounting for several decision makers in the household and for the potential impact of tax reforms on the balance of power. This paper summarizes both the methodological and empirical findings presented in the next three papers of this special issue of the Review of the Economics of the Household. This series of contributions primarily entails a concrete comparison of the policy implications of the choice between the unitary and a particular multi-person representation: the collective representation. On the one hand, it suggests a methodology to implement the collective model of labor supply in a realistic context where participation is modeled together with working hours, and where the full tax-benefit system is accounted for. On the other hand, the empirical part relies on comprehensive simulations of tax reforms in Belgium, France, Germany, Italy, Spain, and the United Kingdom, and allows to quantify the distortions that may affect policy recommendations based on the unitary model.

Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification

Imbalances in covariates between treatment groups are frequent in observational studies and can lead to biased comparisons. Various adjustment methods can be employed to correct these biases in the context of multi-level treatments (> 2). Analytical challenges, such as positivity violations and incorrect model specification due to unknown functional relationships between covariates and treatment or outcome, may affect their ability to yield unbiased results. Such challenges were expected in a comparison of fire-suppression interventions for preventing fire growth. We identified the overlap weights, augmented overlap weights, bias-corrected matching and targeted maximum likelihood as methods with the best potential to address those challenges. A simple variance estimator for the overlap weight estimators that can naturally be combined with machine learning is proposed. In a simulation study, we investigated the performance of these methods as well as those of simpler alternatives. Adjustment methods that included an outcome modeling component performed better than those that focused on the treatment mechanism in our simulations. Additionally, machine learning implementation was observed to efficiently compensate for the unknown model specification for the former methods, but not the latter. Based on these results, we compared the effectiveness of fire-suppression interventions using the augmented overlap weight estimator.     

Reliability analysis of man–machine systems using fuzzy cognitive mapping with genetic tuning

This article offers a method for analyzing the reliability of a man–machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of “influencing factors—reliability,” the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the “driver–car–road” system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly.     

Seeing a talking face matters: Infants' segmentation of continuous auditory-visual speech

Visual speech cues from a speaker's talking face aid speech segmentation in adults, but despite the importance of speech segmentation in language acquisition, little is known about the possible influence of visual speech on infants' speech segmentation. Here, to investigate whether there is facilitation of speech segmentation by visual information, two groups of English-learning 7-month-old infants were presented with continuous speech passages, one group with auditory-only (AO) speech and the other with auditory-visual (AV) speech. Additionally, the possible relation between infants' relative attention to the speaker's mouth versus eye regions and their segmentation performance was examined. Both the AO and the AV groups of infants successfully segmented words from the continuous speech stream, but segmentation performance persisted for longer for infants in the AV group. Interestingly, while AV group infants showed no significant relation between the relative amount of time spent fixating the speaker's mouth versus eyes and word segmentation, their attention to the mouth was greater than that of AO group infants, especially early in test trials. The results are discussed in relation to the possible pathways through which visual speech cues aid speech perception.     

Digital platforms and the changing freelance workforce in the Russian Federation: A ten-year perspective

This article traces the development of the online labour market in the Russian Federation and across the wider post-Soviet space. The authors draw on the unique data of four waves of an online survey for 2009, 2011, 2014 and 2019 on the leading Russian-speaking general-purpose platform for creative and knowledge-based work. The results shed light on key trends, such as spatial decentralization, occupational diversification, feminization, maturing, rising educational attainment and educational mismatch, the consolidation of freelance careers, platformization and legalization. The article discusses these findings and their potential policy implications for the future development of online platform work in the Russian Federation.     

Les plateformes de travail numérique et l‘évolution de la main-d‘œuvre en ligne en Fédération de Russie: une analyse sur dix ans

Les auteurs décrivent l'évolution du marché du travail indépendant en ligne en Fédération de Russie et dans l'espace postsoviétique. Les données proviennent d'une enquête originale, adressée à quatre reprises, entre 2009 et 2019, aux utilisateurs d'une grande plateforme d'intermédiation en langue russe, axée sur les métiers créatifs et l'économie du savoir. Plusieurs tendances se dégagent: expansion de l'aire de recrutement; diversification des spécialisations; féminisation, vieillissement et qualification de la main-d'œuvre; péjoration de l'adéquation profil-tâches; consolidation des parcours indépendants en ligne, plateformisation et formalisation. Les auteurs commentent ces évolutions et formulent des recommandations d'action en vue du développement futur du secteur en Fédération de Russie.