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Interested in the
world of data
storytelling and
curious about the
ins and outs of
data viz? If so,
do
sign up
for
Kontinentalist’s
upcoming 1-day
virtual workshop
on data
storytelling,
taking place on
9 June!
This six-hour
course is perfect
for beginners who
want to learn our
favourite data
storytelling
principles, tips,
and tools. We will
be teaching topics
ranging from
storytelling and
persuasion, data
visualisation, and
design. Seats are
going fast, so
sign up now if
you’re interested!
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Early in 2022, a
group of citizens
approached
Kontinentalist with a
stack of newspapers
about the deaths of
migrant workers in
Singapore, and a
Google Sheet filled
with data they’d been
painstakingly
extracting from
newspapers. In it were
the names, ages, dates
and other information
from each reported
incident which
resulted in a migrant
worker dying.
As with the story on
sexual violence in
Singapore
that we produced last
year, we knew we had a
chance to make a huge
difference; here was a
very important topic
ripe for a data-led
investigation. Though
migrant worker safety
is a ubiquitous issue
in Singapore with an
endless trickle of
reports about their
deaths and injuries, a
comprehensive look at
the underlying
patterns behind their
deaths felt glaringly
absent. Like the
sexual violence story,
there was no publicly
available structured
data with sufficient
granularity.
Our story
was a chance to change
that, and we also
worked in
collaboration with the
group to create the
Migrant Death
Map.
Process
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I refined the
datasheet
and imposed
structure before
continuing the data
collection for the
rest of the 22
years. Structuring
is important because
not all data is
informative. The
data collection and
structuring process
is iterative—for
example, looking at
age and nationality
alone didn't reveal
a pattern that would
give the story legs.
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However, splicing
gender with cause of
the death was
informative, as it
showed the different
modalities of
violence experienced
by men and women
migrant workers.
Iterating between
different degrees of
scope and fidelity
also shapes one’s
understanding and
angle. An example is
deciding the
different categories
of workplace
incidents—is falling
materially different
from being crushed?
What about fires
versus chemical
explosions? We tried
to base it on the
underlying reason
for failure to
protect life that
was more distinct
than other
categories, e.g.,
falling is typically
caused by the
failure to provide
adequate safety
harness attachments
and guardrails.
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Part of our
data
visualisation
on the
jarring
differences
between
expectations
and reality
of a migrant
worker’s
journey.
We backed up the
insights gleaned
from the data with
desk research,
especially the
invaluable reports
and many firsthand
accounts sourced
from NGOs like HOME
and TWC2. This kind
of large-scale
investigative effort
is extremely
resource-intensive
and difficult for
NGOs to do. Yet we
couldn’t have done
it without them.
Achievements like
this truly show how
various routes of
solidarity and
advocacy are needed,
and we can harness
each others’
strengths while
moving towards the
same goals.
Takeaways and
learnings
On a personal level,
it can be very
difficult to do this
work. It is
technically hard to
do well, requiring
estimation and
judgements about
tasks and angles
that have never been
done before. It is
also emotionally
difficult, as one is
immersed in gruesome
and cruel detail
daily for months.
Yet this enterprise
shows it’s very
possible to do this
work with enough
will, empathy, and
curiosity—and,
importantly,
like-minded allies
who care. Truth is
in many ways a
collectively-held
belief; remember
that you always have
the power to
question the only
narrative you’ve
been told (for
example, this is
just the way life
is, these deaths are
a sad economic
necessity, etc.),
and create new
stories from the
data and lived
experiences you
gather.
With our sexual
violence story and
this one on migrant
deaths, we’ve shown
how much can come to
light—and inform
societal interaction
and policy—through
this empirical
approach. Singapore
has a long way to go
in creating usable
databases that
actually push the
envelope in
elevating the
quality of public
discourse on
important matters. I
hope more pressure
is added to make
data collection,
cleaning, and
release a default
across
industries.
On this theme of
labour, let’s take a
moment to rethink
what and who is of
value, why we accept
the status quo of
work, and boldly
imagine what we’d
want it to be.
Sharing a new story
of what is and what
could be is our
first step to a
different and better
future.
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Many households rely
on foreign domestic
workers, but behind
this relationship is
an
uncomfortable
reality.
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E-commerce
has created a culture
of convenience, but at
what cost?
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How equitable is
Singapore’s photography
industry? A team of
photographers created
this
survey
to find out.
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What makes a
multinational
corporation? A data
artist visualises the
hierarchy
of employees at
communications firm
Havas.
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Migrant workers
around the world were
key
during the pandemic, but
their contributions are
largely invisible.
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Who knew assembly lines
would be on trend? Enter
the world of China’s
Factory Tiktok.
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The grim history
behind the term
‘coolie’.
The term coolie
came to rise in the
16th century. The
British used it as a
‘bureaucratic’ term
for indentured
labourers working in
colonial
plantations,1
but the word soon
became associated
with the
exploitation of
migrant workers. In
the family of
Dravidian languages,
the word क़ुली,
kūli, means
wages. In Mandarin,
the word 苦力,
kǔlì, is an
instance of
phono-semantic
matching which
translates to
"bitter strength"
and understood as
"hard labour".2
While there were
supposedly
consensual labour
contracts, the
system was more akin
to modern slavery,
rife with labour
trafficking, human
rights abuses, and
low wages.
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