The march for gender equality of Algerian women: The struggle for spatial and historical recognition

Social and political anti-government movements have been major headlines across the globe in recent years, with a noticeable participation of women. In the MENA region, such movements spanned Egypt, Tunisia, Algeria, and Lebanon among others. Through an ethnographic inquiry into the Algerian pro-democracy movement Hirak (2019–2021), we delve into women's experiences of the Hirak to show how women remain marginalized politically, economically, and socially despite their heavy and praised participation. Using a recognition theory lens, we unveil dialectics of unity and division in the struggle for recognition among women in Algeria, a post-colonial context charged with conflicting ideological stances. We detect two structural dimensions of the struggle, a spatial/physical dimension and a historical/temporal dimension that help surface different gender positionalities and their dynamics as they vie for recognition. We stress the importance of not homogenizing women's political struggles, especially in the Global South.     

Institutions and the shaping of different forms of entrepreneurship

Entrepreneurship is a broad concept encompassing a wide range of activities, from the Schumpertian ideal associated with innovation to simply creating a job for oneself. Because we ask about national differences in entrepreneurship, we consider national differences for entrepreneurship, the institutions, and if these relate to the emergence of different types of enterprise. We propose that national patent grants represent innovation and that national self-employment rates represent job replacement. Interestingly, we found that institutional factors that determine self-employment and innovation may act in opposite directions: what encourages self-employment might discourage innovation and vice-versa.     

Post‐Marxist Trajectories: Diagnosis,Criticism, Utopia*

In this article, I examine the troublesome notion of “post‐Marxism.” First, I read post‐Marxism as an expression of the transformations entailed by our post‐1968 entry into what world‐systems thinkers call “an age of transition.” Second, drawing on Tormey and Townshend's work, I explore the thematic unity of post‐Marxism in terms of six problems posed to Marxism—the problems of history, revolutionary subjectivity, ethics, positivism, vanguardism, and democracy. Third, I suggest that, given the many questions raised by post‐Marxist efforts to go beyond these problems, and given the passing of the phase of what Holmes has called “happy globalization,” serious doubts are raised about the theoretical and utopian gains achieved by post‐Marxism and the distance we might want to travel from the suggestions offered by the Marxian tradition.     

Probabilities of electoral outcomes: from three-candidate to four-candidate elections

The main purpose of this paper is to compute the theoretical likelihood of some electoral outcomes under the impartial anonymous culture in four-candidate elections by using the last versions of software like LattE or Normaliz. By comparison with the three-candidate case, our results allow to analyze the impact of the number of candidates on the occurrence of these voting outcomes.

     

Vegetated roofs in boreal climate support mobile open habitat arthropods,with differentiation between meadow and succulent roofs

Urban Ecosystems - Vegetated roofs are hoped to benefit urban wildlife, yet there are few empirical results regarding the conservation potential of such roofs. In this paper, we focus on arthropods...     

Bayesian non-crossing quantile regression for regularly varying distributions

Quantile regression is a very important statistical tool for predictive modelling and risk assessment. For many applications, conditional quantile at different levels are estimated separately. Consequently the monotonicity of conditional quantiles can be violated when quantile regression curves cross each other. In this paper, we propose a new Bayesian multiple quantile regression based on heavy tailed distribution for non-crossing. We consider a linear quantile regression model for simultaneous Bayesian estimation of multiple quantiles based on a regularly varying assumptions. The numerical and competitive performance of the proposed method is illustrated by simulation.     

Guided Censored Regression

Parametrically guided non‐parametric regression is an appealing method that can reduce the bias of a non‐parametric regression function estimator without increasing the variance. In this paper, we adapt this method to the censored data case using an unbiased transformation of the data and a local linear fit. The asymptotic properties of the proposed estimator are established, and its performance is evaluated via finite sample simulations.     

Two-Term Edgeworth Expansions for the Classes of U- and V-statistics

Much effort has been devoted to deriving Edgeworth expansions for various classes of statistics that are asymptotically normally distributed, with derivations tailored to the individual structure of each class. Expansions with smaller error rates are needed for more accurate statistical inference. Two such Edgeworth expansions are derived analytically in this paper. One is a two-term expansion for the standardized U-statistic of order m, m ? 3, with an error rate o(n^{? 1}). The other is an expansion with the same error rate for the distribution of the standardized V-statistic of the same order. In deriving the Edgeworth expansion, we made use of the close connection between the V- and U-statistics, which permits to first derive the needed expansion for the related U-statistic, then extend it to the V-statistic, taking into consideration the estimation of all difference terms between the two statistics.     

Asymptotic behaviour of M‐estimators in AR(p) models under nonstandard conditions

The authors derive the limiting distribution of M‐estimators in AR(p) models under nonstandard conditions, allowing for discontinuities in score and density functions. Unlike usual regularity assumptions, these conditions are satisfied in the context of L₁‐estimation and autoregression quantiles. The asymptotic distributions of the resulting estimators, however, are not generally Gaussian. Moreover, their bootstrap approximations are consistent along very specific sequences of bootstrap sample sizes only.     

The Adaptive Gril Estimator with a Diverging Number of Parameters

We consider the problem of variables selection and estimation in linear regression model in situations where the number of parameters diverges with the sample size. We propose the adaptive Generalized Ridge-Lasso (mboxAdaGril) which is an extension of the the adaptive Elastic Net. AdaGril incorporates information redundancy among correlated variables for model selection and estimation. It combines the strengths of the quadratic regularization and the adaptively weighted Lasso shrinkage. In this article, we highlight the grouped selection property for AdaCnet method (one type of AdaGril) in the equal correlation case. Under weak conditions, we establish the oracle property of AdaGril which ensures the optimal large performance when the dimension is high. Consequently, it achieves both goals of handling the problem of collinearity in high dimension and enjoys the oracle property. Moreover, we show that AdaGril estimator achieves a Sparsity Inequality, i.e., a bound in terms of the number of non-zero components of the “true” regression coefficient. This bound is obtained under a similar weak Restricted Eigenvalue (RE) condition used for Lasso. Simulations studies show that some particular cases of AdaGril outperform its competitors.