Against All Odds, Femicide Did Not Increase During the First Year of the COVID-19 Pandemic: Evidence From Six Spanish-Speaking Countries

This paper tests a situational hypothesis which postulates that the number of femicides should increase as an unintended consequence of the COVID-19-related lockdowns. The monthly data on femicides from 2017 to 2020 collected in six Spanish-speaking countries—Argentina, Chile, Paraguay, Panama, Mexico, and Spain—and analyzed using threshold models indicate that the hypothesis must be rejected. The total number of femicides in 2020 was similar to that recorded during each of the three previous years, and femicides did not peak during the months of the strictest lockdowns. In fact, their monthly distribution in 2020 did not differ from the seasonal distribution of femicides in any former year. The discussion criticizes the current state of research on femicide and its inability to inspire effective criminal polices. It also proposes three lines of intervention. The latter are based on a holistic approach that places femicide in the context of crimes against persons, incorporates biology and neuroscience approaches, and expands the current cultural explanations of femicide.

Integration of observed and model derived groundwater levels in landslide threshold models in Rwanda

Incorporation of specific regional hydrological characteristics in empirical statistical landslide threshold models has considerable potential to improve the quality of landslide predictions towards reliable early warning systems. The objective of this research was to test the value of regional groundwater level information, as a proxy for water storage fluctuations, to improve regional landslide predictions with empirical models based on the concept of threshold levels. Specifically, we investigated: i) the use of a data driven time series approach to model the regional groundwater levels based on short duration monitoring observations; ii) the predictive power of single variable and bilinear threshold landslide prediction models derived from groundwater levels and precipitation. Based on statistical measures of the model fit (R2 and RMSE), the groundwater level dynamics estimated by the transfer function noise time series model are broadly consistent with the observed groundwater levels. The single variable threshold models derived from groundwater levels exhibited the highest landslide prediction power with 82–93 % of true positive alarms despite the quite high rate of false alarms with about 26–38 %. Further combination as bilinear threshold models reduced the rate of false alarms by about 18–28 % at the expense of reduced true alarms by about 9–29 % and thus, being less advantageous than single variable threshold models. In contrast to precipitation based thresholds, relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.

Nonlinear models of the Phillips curve

The recent flattening of the Phillips curve has stimulated new empirical research and theoretical discussions regarding the nonlinear nature of the changes in the parameters. The objective of the present paper is twofold: to detect the relevant type of the implied nonlinearity and look for some general model capable of generating a Phillips curve mimicking the empirical one. We find evidence of a convex US price Phillips curve, from 1961 q1 to 2019 q4, assessed both by piecewise and threshold models. The result presents some degree of novelty regarding the role of supply shocks and model-specific convexities; in addition, it supports the use of a regime-switching macro system. The latter accomplishes three tasks. It can generate a Phillips curve resembling its empirical counterparts; it creates a medium-run endogenous cycle where unemployment is not a NAIRU; finally, it opens new perspectives on economic policy issues.

Behavioral interference or facilitation does not distinguish between competitive and noncompetitive accounts of lexical selection in word production.

One of the major debates in the field of word production is whether lexical selection is competitive or not. For nearly half a century, semantic interference effects in picture naming latencies have been claimed as evidence for competitive (relative threshold) models of lexical selection, while semantic facilitation effects have been claimed as evidence for non-competitive (simple threshold) models instead. In this paper, we use a computational modeling approach to compare the consequences of competitive and noncompetitive selection algorithms for blocked cyclic picture naming latencies, combined with two approaches to representing taxonomic and thematic semantic features. We show that although our simple model can capture both semantic interference and facilitation, the presence or absence of competition in the selection mechanism is unrelated to the polarity of these semantic effects. These results question the validity of prior assumptions and offer new perspectives on the origins of interference and facilitation in language production.

Anticipation-induced social tipping: can the environment be stabilised by social dynamics?

In the past decades, human activities caused global Earth system changes, e.g., climate change or biodiversity loss. Simultaneously, these associated impacts have increased environmental awareness within societies across the globe, thereby leading to dynamical feedbacks between the social and natural Earth system. Contemporary modelling attempts of Earth system dynamics rarely incorporate such co-evolutions and interactions are mostly studied unidirectionally through direct or remembered past impacts. Acknowledging that societies have the additional capability for foresight, this work proposes a conceptual feedback model of socio-ecological co-evolution with the specific construct of anticipation acting as a mediator between the social and natural system. Our model reproduces results from previous sociological threshold models with bistability if one assumes a static environment. Once the environment changes in response to societal behaviour, the system instead converges towards a globally stable, but not necessarily desired, attractor. Ultimately, we show that anticipation of future ecological states then leads to metastability of the system where desired states can persist for a long time. We thereby demonstrate that foresight and anticipation form an important mechanism which, once its time horizon becomes large enough, fosters social tipping towards behaviour that can stabilise the environment and prevents potential socio-ecological collapse.

Jumping over the network threshold of information diffusion: testing the threshold hypothesis of social influence

PurposeSocial influence plays a crucial role in determining the size of information diffusion. Drawing on threshold models, we reformulate the nonlinear threshold hypothesis of social influence.Design/methodology/approachWe test the threshold hypothesis of social influence with a large dataset of information diffusion on social media.FindingsThere exists a bell-shaped relationship between social influence and diffusion size. However, the large network threshold, limited diffusion depth and intense bursts become the bottlenecks that constrain the diffusion size.Practical implicationsThe practice of viral marketing needs innovative strategies to increase information novelty and reduce the excessive network threshold.Originality/valueIn all, this research extends threshold models of social influence and underlines the nonlinear nature of social influence in information diffusion.