1,139 research outputs found
The Role of Emotional Intelligence in Sympathizing with Rape Victims
This study examined the relationships among participants’ emotional intelligence and participants’ sympathy for an alleged rape victim[1], sympathy for a defendant, and verdict in a mock rape case. Participants were 219 (127 female, 92 male) United States jury eligible individuals between the ages of 18 and 66. Participants were given a rape trial summary accompanied by a manipulated emotional facial expression of the alleged rape victim (angry, sad, afraid, or neutral), or no photograph. Participants were asked to render an individual case verdict and complete a questionnaire with measures to test sympathy for the alleged rape victim, sympathy for the defendant, self-emotional intelligence, other-emotional intelligence, and rape-myth acceptance. Results provided evidence that self and other-emotional intelligences are positively correlated; sympathy for rape victim and sympathy for the defendant do have an effect on case verdict; and, participant characteristics including gender, age, and race are predictive of rape myth acceptance, sympathy for the defendant, sympathy for the victim, and emotional intelligence. Further research should expand on emotional intelligence as a juror characteristic in the United States as well as internationally.
[1] The term rape victim, rather than rape survivor, is used in this study to refer to an individual’s victim status in the context of the legal system
Schools as Providing Transformational Goods
In an age of radical innovation, transforming societies, and globalized relationships, our opportunity to unlock human potential has never been more salient. While a variety of approaches have shown promise in this area, achieving this goal at scale has been hampered by thinking and designs that position learning as a process of knowledge transmission and content acquisition. Clearly content has a significant role in increase people potential, but many designs treat context acquisition as necessary and sufficient, neglecting meaningful engagement with one’s life possibilities as an integral part of the learning process. Instead, herein I posit that relevance, use, and ecosystem empowerment are treated as necessary considerations if not the core focus of any innovation designed to unlock human potential. From this anchoring belief, here it is argued that educational designers need to reposition educational innovations less as interventions designed to fix deficient humans, and more as invitations intended to recruit the learner in leveraging that which is being learned to accomplish goals that are important to them
Feasibility and coexistence of large ecological communities
The role of species interactions in controlling the interplay between the stability of ecosystems and their biodiversity is still not well understood. The ability of ecological communities to recover after small perturbations of the species abundances (local asymptotic stability) has been well studied, whereas the likelihood of a community to persist when the conditions change (structural stability) has received much less attention. Our goal is to understand the effects of diversity, interaction strengths and ecological network structure on the volume of parameter space leading to feasible equilibria. We develop a geometrical framework to study the range of conditions necessary for feasible coexistence. We show that feasibility is determined by few quantities describing the interactions, yielding a nontrivial complexity–feasibility relationship. Analysing more than 100 empirical networks, we show that the range of coexistence conditions in mutualistic systems can be analytically predicted. Finally, we characterize the geometric shape of the feasibility domain, thereby identifying the direction of perturbations that are more likely to cause extinctions
How simple rules determine pedestrian behavior and crowd disasters
With the increasing size and frequency of mass events, the study of crowd
disasters and the simulation of pedestrian flows have become important research
areas. Yet, even successful modeling approaches such as those inspired by
Newtonian force models are still not fully consistent with empirical
observations and are sometimes hard to calibrate. Here, a novel cognitive
science approach is proposed, which is based on behavioral heuristics. We
suggest that, guided by visual information, namely the distance of obstructions
in candidate lines of sight, pedestrians apply two simple cognitive procedures
to adapt their walking speeds and directions. While simpler than previous
approaches, this model predicts individual trajectories and collective patterns
of motion in good quantitative agreement with a large variety of empirical and
experimental data. This includes the emergence of self-organization phenomena,
such as the spontaneous formation of unidirectional lanes or stop-and-go waves.
Moreover, the combination of pedestrian heuristics with body collisions
generates crowd turbulence at extreme densities-a phenomenon that has been
observed during recent crowd disasters. By proposing an integrated treatment of
simultaneous interactions between multiple individuals, our approach overcomes
limitations of current physics-inspired pair interaction models. Understanding
crowd dynamics through cognitive heuristics is therefore not only crucial for a
better preparation of safe mass events. It also clears the way for a more
realistic modeling of collective social behaviors, in particular of human
crowds and biological swarms. Furthermore, our behavioral heuristics may serve
to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA
Information dynamics shape the networks of Internet-mediated prostitution
Like many other social phenomena, prostitution is increasingly coordinated
over the Internet. The online behavior affects the offline activity; the
reverse is also true. We investigated the reported sexual contacts between
6,624 anonymous escorts and 10,106 sex-buyers extracted from an online
community from its beginning and six years on. These sexual encounters were
also graded and categorized (in terms of the type of sexual activities
performed) by the buyers. From the temporal, bipartite network of posts, we
found a full feedback loop in which high grades on previous posts affect the
future commercial success of the sex-worker, and vice versa. We also found a
peculiar growth pattern in which the turnover of community members and sex
workers causes a sublinear preferential attachment. There is, moreover, a
strong geographic influence on network structure-the network is geographically
clustered but still close to connected, the contacts consistent with the
inverse-square law observed in trading patterns. We also found that the number
of sellers scales sublinearly with city size, so this type of prostitution does
not, comparatively speaking, benefit much from an increasing concentration of
people
On Universality in Human Correspondence Activity
Identifying and modeling patterns of human activity has important
ramifications in applications ranging from predicting disease spread to
optimizing resource allocation. Because of its relevance and availability,
written correspondence provides a powerful proxy for studying human activity.
One school of thought is that human correspondence is driven by responses to
received correspondence, a view that requires distinct response mechanism to
explain e-mail and letter correspondence observations. Here, we demonstrate
that, like e-mail correspondence, the letter correspondence patterns of 16
writers, performers, politicians, and scientists are well-described by the
circadian cycle, task repetition and changing communication needs. We confirm
the universality of these mechanisms by properly rescaling letter and e-mail
correspondence statistics to reveal their underlying similarity.Comment: 17 pages, 3 figures, 1 tabl
An Analytical Approach to Neuronal Connectivity
This paper describes how realistic neuromorphic networks can have their
connectivity properties fully characterized in analytical fashion. By assuming
that all neurons have the same shape and are regularly distributed along the
two-dimensional orthogonal lattice with parameter , it is possible to
obtain the accurate number of connections and cycles of any length from the
autoconvolution function as well as from the respective spectral density
derived from the adjacency matrix. It is shown that neuronal shape plays an
important role in defining the spatial spread of network connections. In
addition, most such networks are characterized by the interesting phenomenon
where the connections are progressively shifted along the spatial domain where
the network is embedded. It is also shown that the number of cycles follows a
power law with their respective length. Morphological measurements for
characterization of the spatial distribution of connections, including the
adjacency matrix spectral density and the lacunarity of the connections, are
suggested. The potential of the proposed approach is illustrated with respect
to digital images of real neuronal cells.Comment: 4 pages, 6 figure
Scaling laws of human interaction activity
Even though people in our contemporary, technological society are depending
on communication, our understanding of the underlying laws of human
communicational behavior continues to be poorly understood. Here we investigate
the communication patterns in two social Internet communities in search of
statistical laws in human interaction activity. This research reveals that
human communication networks dynamically follow scaling laws that may also
explain the observed trends in economic growth. Specifically, we identify a
generalized version of Gibrat's law of social activity expressed as a scaling
law between the fluctuations in the number of messages sent by members and
their level of activity. Gibrat's law has been essential in understanding
economic growth patterns, yet without an underlying general principle for its
origin. We attribute this scaling law to long-term correlation patterns in
human activity, which surprisingly span from days to the entire period of the
available data of more than one year. Further, we provide a mathematical
framework that relates the generalized version of Gibrat's law to the long-term
correlated dynamics, which suggests that the same underlying mechanism could be
the source of Gibrat's law in economics, ranging from large firms, research and
development expenditures, gross domestic product of countries, to city
population growth. These findings are also of importance for designing
communication networks and for the understanding of the dynamics of social
systems in which communication plays a role, such as economic markets and
political systems.Comment: 20+7 pages, 4+2 figure
Persistence and Uncertainty in the Academic Career
Understanding how institutional changes within academia may affect the
overall potential of science requires a better quantitative representation of
how careers evolve over time. Since knowledge spillovers, cumulative advantage,
competition, and collaboration are distinctive features of the academic
profession, both the employment relationship and the procedures for assigning
recognition and allocating funding should be designed to account for these
factors. We study the annual production n_{i}(t) of a given scientist i by
analyzing longitudinal career data for 200 leading scientists and 100 assistant
professors from the physics community. We compare our results with 21,156
sports careers. Our empirical analysis of individual productivity dynamics
shows that (i) there are increasing returns for the top individuals within the
competitive cohort, and that (ii) the distribution of production growth is a
leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our
methodology is general, and we speculate that similar features appear in other
disciplines where academic publication is essential and collaboration is a key
feature. We introduce a model of proportional growth which reproduces these two
observations, and additionally accounts for the significantly right-skewed
distributions of career longevity and achievement in science. Using this
theoretical model, we show that short-term contracts can amplify the effects of
competition and uncertainty making careers more vulnerable to early
termination, not necessarily due to lack of individual talent and persistence,
but because of random negative production shocks. We show that fluctuations in
scientific production are quantitatively related to a scientist's collaboration
radius and team efficiency.Comment: 29 pages total: 8 main manuscript + 4 figs, 21 SI text + fig
Compressive Strength of 24S-T Aluminum-alloy Flat Panels with Longitudinal Formed Hat-section Stiffeners
Results are presented for a part of a test program on 24S-T aluminum alloy flat compression panels with longitudinal formed hat-section stiffeners. This part of the program is concerned with panels in which the thickness of the stiffener materials is 0.625 times the skin thickness. The results, presented in tabular and graphical form, show the effect of the relative dimensions of the panel on the buckling stress and the average stress at maximum load. Comparative envelope curves are presented for hat-stiffened and Z-stiffened panels having the same ratio of stiffener thickness to sheet thickness. These curves provide some indication of the relative structural efficiencies of the two types of panel
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