History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg
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0:26 - 0:28I'm here to tell you
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0:28 - 0:31why I don't tell the truth about castles.
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0:33 - 0:35You might think it's my job.
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0:35 - 0:37After all, we expect professionals
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0:37 - 0:40to speak with authority
and give us clear-cut solutions, -
0:40 - 0:42and that makes us very, very nervous
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0:42 - 0:45because there's so much
we simply don't know about history. -
0:46 - 0:47And as a result,
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0:47 - 0:51a lot of things have become established
in our collective memory as the truth -
0:51 - 0:53simply because someone said it once,
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0:53 - 0:55it sounded convincing,
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0:55 - 0:57and nobody since has stood up to say,
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0:57 - 1:00"Well, we don't know exactly
what it was like, -
1:00 - 1:01but it wasn't like that."
-
1:03 - 1:05Take Greek temples.
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1:05 - 1:09Everyone knows they're made
of beautiful shining white marble. -
1:09 - 1:12We've seen them that way for centuries,
from postcards to museums, -
1:13 - 1:16and that establishes certain
seeing habits in our heads, -
1:17 - 1:22where we've seen them this way so much
that anything different just looks wrong. -
1:22 - 1:25And yet today we know for a fact
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1:25 - 1:27that they were painted
in bright garish colors; -
1:27 - 1:30we're just a little unclear
on some of the details. -
1:32 - 1:36I've colored this one in myself
in about five minutes of research, -
1:36 - 1:39so it's likely to be wrong
in all the relevant places, -
1:39 - 1:42and it's still more correct
than the white one. -
1:43 - 1:46So why do we continue
to show them in white? -
1:46 - 1:47Well, there's two reasons for that:
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1:47 - 1:51One is that we as humans like certainty.
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1:51 - 1:55And so we would prefer
to be absolutely certain -
1:55 - 1:58even if it's the absolute certainty
that we are absolutely wrong -
1:58 - 1:59(Laughter)
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1:59 - 2:01than to say, "Well,
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2:01 - 2:04maybe it could have been approximately,
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2:04 - 2:06I think, something like …"
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2:06 - 2:07And then there's the fact
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2:07 - 2:10that when we're trying to establish
a new truth in people's heads, -
2:10 - 2:13we want it to be
the correct truth this time. -
2:14 - 2:17But even if we're not
entirely clear on all the details, -
2:17 - 2:19that doesn't mean
we can't make a statement. -
2:19 - 2:21If you ask me right now what time it is,
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2:21 - 2:23I can't tell you,
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2:23 - 2:28but I don't have to shrug my shoulders
and just say, "I have no idea." -
2:28 - 2:31I know this event is on
from 12:00 till 6:00, -
2:31 - 2:33so that eliminates
half the clock right there. -
2:34 - 2:37We've had our first coffee break,
we've not had the second, -
2:37 - 2:39so it's between 2:00 and 5:00.
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2:40 - 2:45I know there were people ahead of me,
and I'm not being told I'm out of time, -
2:45 - 2:48so it must be around 4:30.
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2:49 - 2:51Is that correct?
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2:51 - 2:52I don't know.
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2:52 - 2:55It might not be the truth,
but I don't have to tell you the truth. -
2:55 - 2:58I just have to know
how correct I'm likely to be -
2:59 - 3:02because how correct I am
can be very, very important. -
3:02 - 3:05Me telling you it's about 4:30
is pretty useless -
3:05 - 3:07if you want to know
whether you can still catch your bus; -
3:07 - 3:10and in that case,
we might have to ask more people, -
3:10 - 3:13we might have to fill it in
with more clues and so on; -
3:13 - 3:14and that's science.
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3:14 - 3:15We ask a question,
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3:15 - 3:19and then we fill in the unknown
and get more and more precise. -
3:19 - 3:22So the scientific method
is pretty well-established: -
3:22 - 3:25You ask a question
about the world around you, -
3:25 - 3:27you research what
you already know about it, -
3:27 - 3:31you design an experiment
to test what you don't know about it, -
3:31 - 3:35you gather the data, you analyze them,
and you reach a conclusion; -
3:35 - 3:37and that conclusion could be,
"I need more data." -
3:37 - 3:40Then you go back,
design another experiment, -
3:40 - 3:41run it again, gather more data,
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3:41 - 3:44and you get more data
and more data and more data, -
3:44 - 3:45and suddenly you're buried in data,
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3:45 - 3:47and you're dealing with big data,
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3:47 - 3:49where scientists now have this problem
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3:49 - 3:52that there's so many data
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3:52 - 3:54they can never read them all
in one lifetime. -
3:54 - 3:56They have to find new ways
to deal with that. -
3:57 - 3:58And then there's me.
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3:59 - 4:00This is me.
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4:00 - 4:03You can tell I'm not
the kind of scientist with a lab coat, -
4:03 - 4:06and my data problem is slightly different.
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4:07 - 4:10Basically I'm dealing
with one student's lab report -
4:10 - 4:12that they dropped on the floor,
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4:12 - 4:14lost half the pages
and then shuffled the rest, -
4:14 - 4:17and there's probably a coffee stain
on the relevant bit. -
4:17 - 4:20So what I've got is I've got
half a broken castle, -
4:20 - 4:22slightly burned,
-
4:23 - 4:27I've got a legal contract from 1388
that was written by a guy -
4:27 - 4:31who managed to spell the name "Arnold"
four different ways in three pages, -
4:32 - 4:33I've got some rocks from the village
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4:33 - 4:36that may or may not have
belonged to this castle, -
4:37 - 4:39I have got a map
that was done by a guy -
4:39 - 4:43for whom this was a 10-minute squiggle
in an eighth-year campaign, -
4:43 - 4:46a painting that was drawn
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4:46 - 4:49about 300 years
after the castle burnt down, -
4:49 - 4:52and a book that was probably propaganda.
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4:52 - 4:54And of course I could go
look in the archives, -
4:54 - 4:57I can get another archaeological
excavation going, and so on, -
4:57 - 5:00but at some point, there's simply
no way to gather more data. -
5:00 - 5:03And then you expect me to take that
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5:03 - 5:06and mash it all up
into the truth about castles? -
5:09 - 5:12You want a reconstruction
that's so realistic -
5:12 - 5:13it feels like you're really there,
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5:13 - 5:17like every little pebble
in the courtyard is just right. -
5:18 - 5:20There's a reason that a lot
of sites and museums -
5:20 - 5:22don't use the word "reconstruction";
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5:22 - 5:24instead, you find a picture,
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5:24 - 5:28and next to it, it has the disclaimer
"Artist's impression." -
5:28 - 5:31And that doesn't mean
they didn't do any research; -
5:31 - 5:33it just means they didn't document
what they researched. -
5:33 - 5:36We don't know who they talked to,
which books they read, -
5:36 - 5:38which conclusions they drew,
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5:38 - 5:40and which other theories they discarded.
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5:40 - 5:42Now, imagine for a moment
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5:42 - 5:45that we would treat a text the same way.
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5:45 - 5:46You go into the museum.
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5:46 - 5:50There's a plaque, and it says,
"Author's impression." -
5:50 - 5:53The author thinks there might
have been a castle here. -
5:53 - 5:55You wouldn't take that very seriously.
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5:55 - 5:59So why do we treat text
so differently from models? -
5:59 - 6:04It's because we've come to a consensus
on what makes a scientific text, -
6:04 - 6:06and it's quite simply this.
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6:06 - 6:08When you're writing
a scientific document, -
6:08 - 6:10you put in footnotes,
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6:10 - 6:12you cite works by previous scholars,
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6:13 - 6:14you show your argumentation -
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6:16 - 6:18you simply give your document provenance -
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6:19 - 6:22because showing you a picture of the truth
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6:22 - 6:26isn't going to help you
without me explaining why it's true. -
6:26 - 6:30The truth is, all of these
are correct at the same time. -
6:31 - 6:33That's the truth,
but it's not a very helpful truth, -
6:35 - 6:39because without context,
data are not information. -
6:39 - 6:41So I'll give you a little context.
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6:44 - 6:45So for a little context,
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6:45 - 6:48this first clock shows
the time in Luxembourg, -
6:48 - 6:51and the second one
has the time in Tokyo, -
6:51 - 6:53the third one is one
of those annoying clocks -
6:53 - 6:56everyone had in their kitchens
about 10 years ago -
6:56 - 6:57that actually run counterclockwise,
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6:57 - 7:00and the fourth one is not a clock,
it's a barometer - -
7:00 - 7:02you just wouldn't know that
by looking at it. -
7:04 - 7:05So in historic research,
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7:05 - 7:07when we deal with images,
we know what to do: -
7:07 - 7:11We give those provenance
through metadata and paradata. -
7:11 - 7:13Metadata you've probably heard.
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7:14 - 7:16Metadata are data about the data.
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7:16 - 7:19You can see those when
you're browsing your computer, -
7:19 - 7:21and you can see who made a file,
when it was made, -
7:21 - 7:23when it was last opened, and so on.
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7:23 - 7:25Paradata are slightly more complex.
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7:25 - 7:28Paradata are data
that give context for the data, -
7:28 - 7:31so like how they were gathered,
how they were processed, -
7:31 - 7:33which decisions were made
about them, and so on. -
7:34 - 7:36The metadata for this image
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7:36 - 7:40would be that it was taken by me
on the first of June, 2017 -
7:40 - 7:43on a Sony compact camera.
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7:43 - 7:47The paradata are that it was
picture 111 in a series of 128 -
7:47 - 7:51and I took it on my first
research trip to this castle. -
7:51 - 7:53And I love to show this picture
-
7:53 - 7:58because this picture has everything in it
that is wrong with models. -
7:59 - 8:02You walk up the stairs in this castle,
you come to the attic, -
8:02 - 8:05and there's a big glass box
with this model sitting in it. -
8:05 - 8:06And what I love about it
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8:06 - 8:09is that there are no data
attached to it whatsoever. -
8:09 - 8:11You don't have a scale bar.
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8:12 - 8:15You don't have a date
it was made or who made it. -
8:15 - 8:17You don't have a date
it's supposed to represent. -
8:17 - 8:19There's nothing even to say
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8:19 - 8:22that it's supposed to be this castle
that you're standing in. -
8:22 - 8:25And if you're talking
about decision-making processes -
8:25 - 8:26in the reconstruction,
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8:26 - 8:29if you take a closer look
at that center tower there, -
8:29 - 8:30it becomes very, very obvious
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8:30 - 8:34the size of that tower was not based
on an archeological excavation -
8:34 - 8:36or because there was
a foundation there or something. -
8:36 - 8:39No, that's the size
of the toilet paper roll they had. -
8:39 - 8:41(Laughter)
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8:41 - 8:43And so this model makes me happy
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8:43 - 8:46because it's everything
I'm trying to avoid. -
8:48 - 8:51And I'm not the only person
trying to avoid this kind of thing. -
8:51 - 8:54A lot of intelligent people
are working and avoiding this. -
8:55 - 8:58There are some hugely
complex systems these days -
8:58 - 9:00that go into great detail on data,
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9:00 - 9:03metadata, paradata,
how they all relate, and so forth; -
9:03 - 9:06and my favorite one
takes about six months to learn. -
9:07 - 9:09Now that's bad enough for me
as a researcher, -
9:09 - 9:11but imagine that you,
as a museum visitor, -
9:11 - 9:13have to go on a six-month
training course -
9:13 - 9:15to understand what you're seeing.
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9:16 - 9:20So, instead, I have a system
that's just good enough for me. -
9:20 - 9:22I simply take my model,
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9:22 - 9:27and I tell you which parts are true
and which ones are not. -
9:27 - 9:29So probably true is the easiest.
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9:29 - 9:32That's the category of things
that I think are true -
9:32 - 9:34because they're still there,
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9:34 - 9:37so that could be things
like the castle ruins. -
9:38 - 9:40Next, pretty close to true,
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9:41 - 9:43we have a lot of evidence for those.
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9:43 - 9:44So for example,
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9:44 - 9:47I was saying foundations,
towers on foundations - -
9:47 - 9:50we fill in the gaps
what we have good evidence. -
9:50 - 9:54Third stage, extrapolation,
could be true - maybe not. -
9:54 - 9:57That's where I'm working
on secondary and tertiary data, -
9:57 - 9:58like the maps and images.
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9:58 - 10:00And then there's my favorite category -
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10:01 - 10:03the stuff that's not really true.
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10:05 - 10:07Now, these things I need
to put in my model -
10:08 - 10:10because the model would be
missing something without it. -
10:10 - 10:13If I didn't put these in,
I would be telling you a lie, -
10:14 - 10:16but I have no idea what to really put in.
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10:16 - 10:18It's an interesting problem.
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10:18 - 10:22So that's things like I know
the great hall had paintings on the walls, -
10:22 - 10:24I will never know what exactly
was painted on them, -
10:24 - 10:26so I have to make something up,
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10:26 - 10:29but if I left them as a blank stone
the way they are now, -
10:29 - 10:31that would be making a statement.
-
10:31 - 10:35And then, of course, I need to attach
my metadata and my paradata, -
10:35 - 10:38and tell you why it's in that category.
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10:38 - 10:41And finally, I need
to make very, very sure -
10:41 - 10:44that you don't only know
why it's in that category -
10:44 - 10:46but which part exactly
I'm talking about. -
10:46 - 10:49If you remember that clock from earlier,
-
10:49 - 10:53well, I can tell you for a fact
that it's Friday afternoon. -
10:53 - 10:56I can also tell you
with absolute certainty -
10:56 - 11:00that sometime in the last two millennia,
we had a castle on this hill. -
11:01 - 11:03What I cannot tell you
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11:03 - 11:06is whether in that window, in 1548,
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11:06 - 11:10we had an archway
and that archway had a stone -
11:10 - 11:13and that stone was exactly
312 millimeters wide. -
11:13 - 11:15It could have been 317,
-
11:15 - 11:18but my drawing is going to say
one way or the other. -
11:20 - 11:24And that is the really, really
interesting point for future researchers -
11:24 - 11:27because if I've told you
I have no idea what was here, -
11:27 - 11:30they can use that point to research,
and then they can say, -
11:30 - 11:33"Look, we found more data,
and actually you're completely wrong. -
11:33 - 11:35It was 483 millimeters."
-
11:35 - 11:37And I can say, "Hooray!"
-
11:37 - 11:40because that advances
our state of collective knowledge. -
11:40 - 11:42So if I'm doing science properly,
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11:42 - 11:45I want people to be able
to prove me wrong. -
11:46 - 11:50So that's why I'm not going to tell you
the truth about castles, -
11:51 - 11:54and why I make it
very, very clear to you -
11:55 - 11:56when I'm just making it up.
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11:56 - 11:58(Laughter)
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11:58 - 12:00Thank you.
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12:00 - 12:02(Applause)
- Title:
- History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg
- Description:
-
"My topic is the importance of admitting that we don’t know everything, and not being too focused on telling the truth. It’s perfectly fine--in fact, it’s good scientific practice!--to say when we know something and when we’re only speculating. Like all sciences, the humanities need to move away from the “black box” model of knowledge creation."
Marleen has been bouncing around between European countries and different disciplines, completing a degree in Architecture at the Technical University of Wuppertal, Germany, a degree in Heritage Science at Queen’s University, Belfast, Northern Ireland and a Marie Sklodowska Curie Fellowship in Digital Cultural Heritage in Vienna, Austria. She’s now working toward a PhD in History at the University of Luxembourg, where she is doing virtual reconstructions of castles.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDxTalks
- Duration:
- 12:12
Peter van de Ven edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Peter van de Ven edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Peter van de Ven approved English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Peter van de Ven accepted English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Peter van de Ven edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Amanda Chu edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Amanda Chu edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg | ||
Amanda Chu edited English subtitles for History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg |