Beyond fear of backlash: Effects of messages about structural drivers of COVID-19 disparities among large samples of Asian, Black, Hispanic, and White Americans
Social Science & Medicine,
Journal Year:
2025,
Volume and Issue:
unknown, P. 118096 - 118096
Published: April 1, 2025
Language: Английский
The effect of seeing scientists as intellectually humble on trust in scientists and their research
Nature Human Behaviour,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 18, 2024
Language: Английский
When expert predictions fail
Trends in Cognitive Sciences,
Journal Year:
2023,
Volume and Issue:
28(2), P. 113 - 123
Published: Nov. 8, 2023
Language: Английский
Going beyond political ideology: A computational analysis of civic trust in science
Public Understanding of Science,
Journal Year:
2024,
Volume and Issue:
33(8), P. 1046 - 1062
Published: April 24, 2024
Numerous
studies
have
been
conducted
to
identify
the
factors
that
predict
trust/distrust
in
science.
However,
most
of
these
are
based
on
closed-ended
survey
research,
which
does
not
allow
researchers
gain
a
more
nuanced
understanding
phenomenon.
This
study
integrated
analysis
within
United
States
with
computational
text
reveal
previously
obscured
by
traditional
methodologies.
Even
after
controlling
for
political
ideology—which
has
significant
explanatory
factor
determining
trust
science
framework—we
found
those
concerns
over
boundary-crossing
(i.e.
or
perceptions
overlaps
politics,
government,
and
funding)
were
less
likely
than
their
counterparts.
Language: Английский
Insights into accuracy of social scientists' forecasts of societal change
Published: Sept. 12, 2022
How
well
can
social
scientists
predict
societal
change,
and
what
processes
underlie
their
predictions?
To
answer
these
questions,
we
ran
two
forecasting
tournaments
testing
accuracy
of
predictions
change
in
domains
commonly
studied
the
sciences:
ideological
preferences,
political
polarization,
life
satisfaction,
sentiment
on
media,
gender-career
racial
bias.
Following
provision
historical
trend
data
domain,
submitted
pre-registered
monthly
forecasts
for
a
year
(Tournament
1;
N=86
teams/359
forecasts),
with
an
opportunity
to
update
based
new
six
months
later
2;
N=120
teams/546
forecasts).
Benchmarking
revealed
that
scientists’
were
average
no
more
accurate
than
simple
statistical
models
(historical
means,
random
walk,
or
linear
regressions)
aggregate
sample
from
general
public
(N=802).
However,
if
they
had
scientific
expertise
prediction
interdisciplinary,
used
simpler
models,
prior
data.
Language: Английский
Reflecting on Past Theoretical Contributions in Psychological Science: A New Initiative
Psychological Inquiry,
Journal Year:
2024,
Volume and Issue:
35(1), P. 1 - 2
Published: Jan. 2, 2024
Language: Английский
The time trap: Distinguishing between-person traits from within-person change in wisdom and well-being after adversity
Published: March 8, 2023
This
study
tests
three
common
assumptions
in
research
on
complex
thought
and
wisdom:
cause
of
indicators,
cross-situational
consistency,
between-within
isomorphism.
Using
a
year-long,
multi-wave
499
North
Americans’
event-contingent
reflections
autobiographical
adversity,
we
examined
intellectual
humility,
open-mindedness,
perspective
taking,
search
for
compromise/conflict
resolution.
Network
models
outperformed
factor
models,
questioning
the
assumption.
Wisdom-related
features
showed
lower
stability
than
personality
traits
subjective
well-being,
challenging
consistency.
Between-person
within-person
associations
differed,
violating
isomorphism
Longitudinal
analyses
further
revealed
that
change
self-distancing
perceived
level
distress,
but
not
other
proposed
moderators,
were
associated
with
growth
several
wisdom
months
later.
These
results
call
revision
approaches
to
studying
highlighting
importance
longitudinal
more
precise
temporal
claims
psychological
science.
Language: Английский
When expert predictions fail
Published: Oct. 13, 2023
We
examine
the
opportunities
and
challenges
of
expert
judgment
in
social
sciences,
scrutinizing
way
scientists
make
predictions.
While
show
above
chance
accuracy
predicting
lab-based
phenomena,
they
often
struggle
to
predict
real-world
societal
changes.
argue
that
most
causal
models
used
sciences
are
oversimplified,
confuse
levels
analysis
which
a
model
applies,
misalign
nature
with
fail
consider
factors
beyond
scientist’s
pet
theory.
Taking
cues
from
physical
meteorology,
we
advocate
for
an
approach
integrates
broad
foundational
context-specific
time
series
data.
call
shift
towards
more
precise,
daring
predictions,
greater
intellectual
humility.
Language: Английский