# Abstract Example

#### Rachel Rogers

#### 2024-08-28

# Fuzzy Collocation of length 5

0
3
Subjective
pattern
comparison
has
been
subject
to
increased
scrutiny
by
the
courts
and
by
the
general
public,
resulting
in
an
increased
interest
in
pattern
comparison
algorithms
that
provide
quantitative
assessments
of
similarity
for
use
by
forensic
scientists.
While
these
algorithms
would
mark
an
improvement
over
current
subjective
comparison
methods,
individuals
without
a
statistical
background
may
struggle
with
the
statistical
concepts
and
language
necessary
for
describing
algorithmic
methods.
If
algorithms
are
to
be
used,
examiners
must
be
able
to
testify
about
their
use
in
a
way
that
is
accessible
to
the
jury.
In
a
series
of
studies,
we
conduct
an
assessment
of
language
and
supporting
visual
aids
which
might
be
used
to
explain
bullet
matching
algorithms.
In
the
initial
study,
we
encountered
a
response
type
calibration
issue
-
individuals
thought
highly
of
the
forensic
witness
and
evidence
regardless
of
experimental
conditions,
’maxing
out’
Likert
response
scales
and
leaving
us
unable
to
tell
if
the
conditions
had
any
effect.
While
this
study
indicated
that
individuals
overall
found
the
testimony
to
be
reliable,
credible,
and
scientific,
it
did
not
readily
provide
information
about
our
question
of
interest.
Additional
data
from
this
study
was
found
in
the
participants’
note
pads.
Through
cleaning
sequential
notes
and
designing
a
method
for
highlighting
study
transcripts
according
to
the
frequency
of
collocations
in
participant
notes,
we
can
determine
which
portions
of
testimony
participants
found
‘noteworthy’.
We
also
conducted
a
study
on
response
types
to
determine
the
consistency
of
participant
responses
across
response
types,
compare
a
variety
of
response
types,
and
determine
which
response
type
may
be
appropriately
calibrated
for
addressing
the
initial
research
question
of
jury
perception
of
algorithms
and
demonstrative
evidence.
The
response
types
used
in
this
investigation
include
the
participant’s
interpretation
of
the
strength
of
evidence
(Likert
scale),
conviction
decision
(binary),
opinion
of
guilt
(binary),
willingness
to
bet
on
their
opinion
of
guilt
(numeric),
probability
of
guilt
(numeric),
and
chance
of
guilt
/innocence
(numeric
or
multiple
choice).
The
note
cleaning,
text
analysis,
and
testimony
tools
we
developed
throughout
this
series
of
experiments
will
benefit
our
future
research
in
jury
perception,
as
well
as
future
transcript
studies.

# Fuzzy Collocation of length 5 with n_gram_width of 2 instead of 4

0
4
Subjective
pattern
comparison
has
been
subject
to
increased
scrutiny
by
the
courts
and
by
the
general
public,
resulting
in
an
increased
interest
in
pattern
comparison
algorithms
that
provide
quantitative
assessments
of
similarity
for
use
by
forensic
scientists.
While
these
algorithms
would
mark
an
improvement
over
current
subjective
comparison
methods,
individuals
without
a
statistical
background
may
struggle
with
the
statistical
concepts
and
language
necessary
for
describing
algorithmic
methods.
If
algorithms
are
to
be
used,
examiners
must
be
able
to
testify
about
their
use
in
a
way
that
is
accessible
to
the
jury.
In
a
series
of
studies,
we
conduct
an
assessment
of
language
and
supporting
visual
aids
which
might
be
used
to
explain
bullet
matching
algorithms.
In
the
initial
study,
we
encountered
a
response
type
calibration
issue
-
individuals
thought
highly
of
the
forensic
witness
and
evidence
regardless
of
experimental
conditions,
’maxing
out’
Likert
response
scales
and
leaving
us
unable
to
tell
if
the
conditions
had
any
effect.
While
this
study
indicated
that
individuals
overall
found
the
testimony
to
be
reliable,
credible,
and
scientific,
it
did
not
readily
provide
information
about
our
question
of
interest.
Additional
data
from
this
study
was
found
in
the
participants’
note
pads.
Through
cleaning
sequential
notes
and
designing
a
method
for
highlighting
study
transcripts
according
to
the
frequency
of
collocations
in
participant
notes,
we
can
determine
which
portions
of
testimony
participants
found
‘noteworthy’.
We
also
conducted
a
study
on
response
types
to
determine
the
consistency
of
participant
responses
across
response
types,
compare
a
variety
of
response
types,
and
determine
which
response
type
may
be
appropriately
calibrated
for
addressing
the
initial
research
question
of
jury
perception
of
algorithms
and
demonstrative
evidence.
The
response
types
used
in
this
investigation
include
the
participant’s
interpretation
of
the
strength
of
evidence
(Likert
scale),
conviction
decision
(binary),
opinion
of
guilt
(binary),
willingness
to
bet
on
their
opinion
of
guilt
(numeric),
probability
of
guilt
(numeric),
and
chance
of
guilt
/innocence
(numeric
or
multiple
choice).
The
note
cleaning,
text
analysis,
and
testimony
tools
we
developed
throughout
this
series
of
experiments
will
benefit
our
future
research
in
jury
perception,
as
well
as
future
transcript
studies.

# Non-Fuzzy Collocation of length 5

0
2
Subjective
pattern
comparison
has
been
subject
to
increased
scrutiny
by
the
courts
and
by
the
general
public,
resulting
in
an
increased
interest
in
pattern
comparison
algorithms
that
provide
quantitative
assessments
of
similarity
for
use
by
forensic
scientists.
While
these
algorithms
would
mark
an
improvement
over
current
subjective
comparison
methods,
individuals
without
a
statistical
background
may
struggle
with
the
statistical
concepts
and
language
necessary
for
describing
algorithmic
methods.
If
algorithms
are
to
be
used,
examiners
must
be
able
to
testify
about
their
use
in
a
way
that
is
accessible
to
the
jury.
In
a
series
of
studies,
we
conduct
an
assessment
of
language
and
supporting
visual
aids
which
might
be
used
to
explain
bullet
matching
algorithms.
In
the
initial
study,
we
encountered
a
response
type
calibration
issue
-
individuals
thought
highly
of
the
forensic
witness
and
evidence
regardless
of
experimental
conditions,
’maxing
out’
Likert
response
scales
and
leaving
us
unable
to
tell
if
the
conditions
had
any
effect.
While
this
study
indicated
that
individuals
overall
found
the
testimony
to
be
reliable,
credible,
and
scientific,
it
did
not
readily
provide
information
about
our
question
of
interest.
Additional
data
from
this
study
was
found
in
the
participants’
note
pads.
Through
cleaning
sequential
notes
and
designing
a
method
for
highlighting
study
transcripts
according
to
the
frequency
of
collocations
in
participant
notes,
we
can
determine
which
portions
of
testimony
participants
found
‘noteworthy’.
We
also
conducted
a
study
on
response
types
to
determine
the
consistency
of
participant
responses
across
response
types,
compare
a
variety
of
response
types,
and
determine
which
response
type
may
be
appropriately
calibrated
for
addressing
the
initial
research
question
of
jury
perception
of
algorithms
and
demonstrative
evidence.
The
response
types
used
in
this
investigation
include
the
participant’s
interpretation
of
the
strength
of
evidence
(Likert
scale),
conviction
decision
(binary),
opinion
of
guilt
(binary),
willingness
to
bet
on
their
opinion
of
guilt
(numeric),
probability
of
guilt
(numeric),
and
chance
of
guilt
/innocence
(numeric
or
multiple
choice).
The
note
cleaning,
text
analysis,
and
testimony
tools
we
developed
throughout
this
series
of
experiments
will
benefit
our
future
research
in
jury
perception,
as
well
as
future
transcript
studies.

# Fuzzy Collocation of length 3

0
4
Subjective
pattern
comparison
has
been
subject
to
increased
scrutiny
by
the
courts
and
by
the
general
public,
resulting
in
an
increased
interest
in
pattern
comparison
algorithms
that
provide
quantitative
assessments
of
similarity
for
use
by
forensic
scientists.
While
these
algorithms
would
mark
an
improvement
over
current
subjective
comparison
methods,
individuals
without
a
statistical
background
may
struggle
with
the
statistical
concepts
and
language
necessary
for
describing
algorithmic
methods.
If
algorithms
are
to
be
used,
examiners
must
be
able
to
testify
about
their
use
in
a
way
that
is
accessible
to
the
jury.
In
a
series
of
studies,
we
conduct
an
assessment
of
language
and
supporting
visual
aids
which
might
be
used
to
explain
bullet
matching
algorithms.
In
the
initial
study,
we
encountered
a
response
type
calibration
issue
-
individuals
thought
highly
of
the
forensic
witness
and
evidence
regardless
of
experimental
conditions,
’maxing
out’
Likert
response
scales
and
leaving
us
unable
to
tell
if
the
conditions
had
any
effect.
While
this
study
indicated
that
individuals
overall
found
the
testimony
to
be
reliable,
credible,
and
scientific,
it
did
not
readily
provide
information
about
our
question
of
interest.
Additional
data
from
this
study
was
found
in
the
participants’
note
pads.
Through
cleaning
sequential
notes
and
designing
a
method
for
highlighting
study
transcripts
according
to
the
frequency
of
collocations
in
participant
notes,
we
can
determine
which
portions
of
testimony
participants
found
‘noteworthy’.
We
also
conducted
a
study
on
response
types
to
determine
the
consistency
of
participant
responses
across
response
types,
compare
a
variety
of
response
types,
and
determine
which
response
type
may
be
appropriately
calibrated
for
addressing
the
initial
research
question
of
jury
perception
of
algorithms
and
demonstrative
evidence.
The
response
types
used
in
this
investigation
include
the
participant’s
interpretation
of
the
strength
of
evidence
(Likert
scale),
conviction
decision
(binary),
opinion
of
guilt
(binary),
willingness
to
bet
on
their
opinion
of
guilt
(numeric),
probability
of
guilt
(numeric),
and
chance
of
guilt
/innocence
(numeric
or
multiple
choice).
The
note
cleaning,
text
analysis,
and
testimony
tools
we
developed
throughout
this
series
of
experiments
will
benefit
our
future
research
in
jury
perception,
as
well
as
future
transcript
studies.

# Fuzzy Collocation of length 4

0
4
Subjective
pattern
comparison
has
been
subject
to
increased
scrutiny
by
the
courts
and
by
the
general
public,
resulting
in
an
increased
interest
in
pattern
comparison
algorithms
that
provide
quantitative
assessments
of
similarity
for
use
by
forensic
scientists.
While
these
algorithms
would
mark
an
improvement
over
current
subjective
comparison
methods,
individuals
without
a
statistical
background
may
struggle
with
the
statistical
concepts
and
language
necessary
for
describing
algorithmic
methods.
If
algorithms
are
to
be
used,
examiners
must
be
able
to
testify
about
their
use
in
a
way
that
is
accessible
to
the
jury.
In
a
series
of
studies,
we
conduct
an
assessment
of
language
and
supporting
visual
aids
which
might
be
used
to
explain
bullet
matching
algorithms.
In
the
initial
study,
we
encountered
a
response
type
calibration
issue
-
individuals
thought
highly
of
the
forensic
witness
and
evidence
regardless
of
experimental
conditions,
’maxing
out’
Likert
response
scales
and
leaving
us
unable
to
tell
if
the
conditions
had
any
effect.
While
this
study
indicated
that
individuals
overall
found
the
testimony
to
be
reliable,
credible,
and
scientific,
it
did
not
readily
provide
information
about
our
question
of
interest.
Additional
data
from
this
study
was
found
in
the
participants’
note
pads.
Through
cleaning
sequential
notes
and
designing
a
method
for
highlighting
study
transcripts
according
to
the
frequency
of
collocations
in
participant
notes,
we
can
determine
which
portions
of
testimony
participants
found
‘noteworthy’.
We
also
conducted
a
study
on
response
types
to
determine
the
consistency
of
participant
responses
across
response
types,
compare
a
variety
of
response
types,
and
determine
which
response
type
may
be
appropriately
calibrated
for
addressing
the
initial
research
question
of
jury
perception
of
algorithms
and
demonstrative
evidence.
The
response
types
used
in
this
investigation
include
the
participant’s
interpretation
of
the
strength
of
evidence
(Likert
scale),
conviction
decision
(binary),
opinion
of
guilt
(binary),
willingness
to
bet
on
their
opinion
of
guilt
(numeric),
probability
of
guilt
(numeric),
and
chance
of
guilt
/innocence
(numeric
or
multiple
choice).
The
note
cleaning,
text
analysis,
and
testimony
tools
we
developed
throughout
this
series
of
experiments
will
benefit
our
future
research
in
jury
perception,
as
well
as
future
transcript
studies.