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← History and other lies | Marleen De Kramer | TEDxUniversityofLuxembourg

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Showing Revision 8 created 02/17/2020 by Peter van de Ven.

  1. I'm here to tell you
  2. why I don't tell the truth about castles.
  3. You might think it's my job.
  4. After all, we expect professionals
  5. to speak with authority
    and give us clear-cut solutions,
  6. and that makes us very, very nervous
  7. because there's so much
    we simply don't know about history.
  8. And as a result,
  9. a lot of things have become established
    in our collective memory as the truth
  10. simply because someone said it once,
  11. it sounded convincing,
  12. and nobody since has stood up to say,
  13. "Well, we don't know exactly
    what it was like,
  14. but it wasn't like that."
  15. Take Greek temples.
  16. Everyone knows they're made
    of beautiful shining white marble.
  17. We've seen them that way for centuries,
    from postcards to museums,
  18. and that establishes certain
    seeing habits in our heads,
  19. where we've seen them this way so much
    that anything different just looks wrong.
  20. And yet today we know for a fact
  21. that they were painted
    in bright garish colors;
  22. we're just a little unclear
    on some of the details.
  23. I've colored this one in myself
    in about five minutes of research,
  24. so it's likely to be wrong
    in all the relevant places,
  25. and it's still more correct
    than the white one.
  26. So why do we continue
    to show them in white?
  27. Well, there's two reasons for that:
  28. One is that we as humans like certainty.
  29. And so we would prefer
    to be absolutely certain
  30. even if it's the absolute certainty
    that we are absolutely wrong
  31. (Laughter)
  32. than to say, "Well,
  33. maybe it could have been approximately,
  34. I think, something like …"
  35. And then there's the fact
  36. that when we're trying to establish
    a new truth in people's heads,
  37. we want it to be
    the correct truth this time.
  38. But even if we're not
    entirely clear on all the details,
  39. that doesn't mean
    we can't make a statement.
  40. If you ask me right now what time it is,
  41. I can't tell you,
  42. but I don't have to shrug my shoulders
    and just say, "I have no idea."
  43. I know this event is on
    from 12:00 till 6:00,
  44. so that eliminates
    half the clock right there.
  45. We've had our first coffee break,
    we've not had the second,
  46. so it's between 2:00 and 5:00.
  47. I know there were people ahead of me,
    and I'm not being told I'm out of time,
  48. so it must be around 4:30.
  49. Is that correct?
  50. I don't know.
  51. It might not be the truth,
    but I don't have to tell you the truth.
  52. I just have to know
    how correct I'm likely to be
  53. because how correct I am
    can be very, very important.
  54. Me telling you it's about 4:30
    is pretty useless
  55. if you want to know
    whether you can still catch your bus;
  56. and in that case,
    we might have to ask more people,
  57. we might have to fill it in
    with more clues and so on;
  58. and that's science.
  59. We ask a question,
  60. and then we fill in the unknown
    and get more and more precise.
  61. So the scientific method
    is pretty well-established:
  62. You ask a question
    about the world around you,
  63. you research what
    you already know about it,
  64. you design an experiment
    to test what you don't know about it,
  65. you gather the data, you analyze them,
    and you reach a conclusion;
  66. and that conclusion could be,
    "I need more data."
  67. Then you go back,
    design another experiment,
  68. run it again, gather more data,
  69. and you get more data
    and more data and more data,
  70. and suddenly you're buried in data,
  71. and you're dealing with big data,
  72. where scientists now have this problem
  73. that there's so many data
  74. they can never read them all
    in one lifetime.
  75. They have to find new ways
    to deal with that.
  76. And then there's me.
  77. This is me.
  78. You can tell I'm not
    the kind of scientist with a lab coat,
  79. and my data problem is slightly different.
  80. Basically I'm dealing
    with one student's lab report
  81. that they dropped on the floor,
  82. lost half the pages
    and then shuffled the rest,
  83. and there's probably a coffee stain
    on the relevant bit.
  84. So what I've got is I've got
    half a broken castle,
  85. slightly burned,
  86. I've got a legal contract from 1388
    that was written by a guy
  87. who managed to spell the name "Arnold"
    four different ways in three pages,
  88. I've got some rocks from the village
  89. that may or may not have
    belonged to this castle,
  90. I have got a map
    that was done by a guy
  91. for whom this was a 10-minute squiggle
    in an eighth-year campaign,
  92. a painting that was drawn
  93. about 300 years
    after the castle burnt down,
  94. and a book that was probably propaganda.
  95. And of course I could go
    look in the archives,
  96. I can get another archaeological
    excavation going, and so on,
  97. but at some point, there's simply
    no way to gather more data.
  98. And then you expect me to take that
  99. and mash it all up
    into the truth about castles?
  100. You want a reconstruction
    that's so realistic
  101. it feels like you're really there,
  102. like every little pebble
    in the courtyard is just right.
  103. There's a reason that a lot
    of sites and museums
  104. don't use the word "reconstruction";
  105. instead, you find a picture,
  106. and next to it, it has the disclaimer
    "Artist's impression."
  107. And that doesn't mean
    they didn't do any research;
  108. it just means they didn't document
    what they researched.
  109. We don't know who they talked to,
    which books they read,
  110. which conclusions they drew,
  111. and which other theories they discarded.
  112. Now, imagine for a moment
  113. that we would treat a text the same way.
  114. You go into the museum.
  115. There's a plaque, and it says,
    "Author's impression."
  116. The author thinks there might
    have been a castle here.
  117. You wouldn't take that very seriously.
  118. So why do we treat text
    so differently from models?
  119. It's because we've come to a consensus
    on what makes a scientific text,
  120. and it's quite simply this.
  121. When you're writing
    a scientific document,
  122. you put in footnotes,
  123. you cite works by previous scholars,
  124. you show your argumentation -
  125. you simply give your document provenance -
  126. because showing you a picture of the truth
  127. isn't going to help you
    without me explaining why it's true.
  128. The truth is, all of these
    are correct at the same time.
  129. That's the truth,
    but it's not a very helpful truth,
  130. because without context,
    data are not information.
  131. So I'll give you a little context.
  132. So for a little context,
  133. this first clock shows
    the time in Luxembourg,
  134. and the second one
    has the time in Tokyo,
  135. the third one is one
    of those annoying clocks
  136. everyone had in their kitchens
    about 10 years ago
  137. that actually run counterclockwise,
  138. and the fourth one is not a clock,
    it's a barometer -
  139. you just wouldn't know that
    by looking at it.
  140. So in historic research,
  141. when we deal with images,
    we know what to do:
  142. We give those provenance
    through metadata and paradata.
  143. Metadata you've probably heard.
  144. Metadata are data about the data.
  145. You can see those when
    you're browsing your computer,
  146. and you can see who made a file,
    when it was made,
  147. when it was last opened, and so on.
  148. Paradata are slightly more complex.
  149. Paradata are data
    that give context for the data,
  150. so like how they were gathered,
    how they were processed,
  151. which decisions were made
    about them, and so on.
  152. The metadata for this image
  153. would be that it was taken by me
    on the first of June, 2017
  154. on a Sony compact camera.
  155. The paradata are that it was
    picture 111 in a series of 128
  156. and I took it on my first
    research trip to this castle.
  157. And I love to show this picture
  158. because this picture has everything in it
    that is wrong with models.
  159. You walk up the stairs in this castle,
    you come to the attic,
  160. and there's a big glass box
    with this model sitting in it.
  161. And what I love about it
  162. is that there are no data
    attached to it whatsoever.
  163. You don't have a scale bar.
  164. You don't have a date
    it was made or who made it.
  165. You don't have a date
    it's supposed to represent.
  166. There's nothing even to say
  167. that it's supposed to be this castle
    that you're standing in.
  168. And if you're talking
    about decision-making processes
  169. in the reconstruction,
  170. if you take a closer look
    at that center tower there,
  171. it becomes very, very obvious
  172. the size of that tower was not based
    on an archeological excavation
  173. or because there was
    a foundation there or something.
  174. No, that's the size
    of the toilet paper roll they had.
  175. (Laughter)
  176. And so this model makes me happy
  177. because it's everything
    I'm trying to avoid.
  178. And I'm not the only person
    trying to avoid this kind of thing.
  179. A lot of intelligent people
    are working and avoiding this.
  180. There are some hugely
    complex systems these days
  181. that go into great detail on data,
  182. metadata, paradata,
    how they all relate, and so forth;
  183. and my favorite one
    takes about six months to learn.
  184. Now that's bad enough for me
    as a researcher,
  185. but imagine that you,
    as a museum visitor,
  186. have to go on a six-month
    training course
  187. to understand what you're seeing.
  188. So, instead, I have a system
    that's just good enough for me.
  189. I simply take my model,
  190. and I tell you which parts are true
    and which ones are not.
  191. So probably true is the easiest.
  192. That's the category of things
    that I think are true
  193. because they're still there,
  194. so that could be things
    like the castle ruins.
  195. Next, pretty close to true,
  196. we have a lot of evidence for those.
  197. So for example,
  198. I was saying foundations,
    towers on foundations -
  199. we fill in the gaps
    what we have good evidence.
  200. Third stage, extrapolation,
    could be true - maybe not.
  201. That's where I'm working
    on secondary and tertiary data,
  202. like the maps and images.
  203. And then there's my favorite category -
  204. the stuff that's not really true.
  205. Now, these things I need
    to put in my model
  206. because the model would be
    missing something without it.
  207. If I didn't put these in,
    I would be telling you a lie,
  208. but I have no idea what to really put in.
  209. It's an interesting problem.
  210. So that's things like I know
    the great hall had paintings on the walls,
  211. I will never know what exactly
    was painted on them,
  212. so I have to make something up,
  213. but if I left them as a blank stone
    the way they are now,
  214. that would be making a statement.
  215. And then, of course, I need to attach
    my metadata and my paradata,
  216. and tell you why it's in that category.
  217. And finally, I need
    to make very, very sure
  218. that you don't only know
    why it's in that category
  219. but which part exactly
    I'm talking about.
  220. If you remember that clock from earlier,
  221. well, I can tell you for a fact
    that it's Friday afternoon.
  222. I can also tell you
    with absolute certainty
  223. that sometime in the last two millennia,
    we had a castle on this hill.
  224. What I cannot tell you
  225. is whether in that window, in 1548,
  226. we had an archway
    and that archway had a stone
  227. and that stone was exactly
    312 millimeters wide.
  228. It could have been 317,
  229. but my drawing is going to say
    one way or the other.
  230. And that is the really, really
    interesting point for future researchers
  231. because if I've told you
    I have no idea what was here,
  232. they can use that point to research,
    and then they can say,
  233. "Look, we found more data,
    and actually you're completely wrong.
  234. It was 483 millimeters."
  235. And I can say, "Hooray!"
  236. because that advances
    our state of collective knowledge.
  237. So if I'm doing science properly,
  238. I want people to be able
    to prove me wrong.
  239. So that's why I'm not going to tell you
    the truth about castles,
  240. and why I make it
    very, very clear to you
  241. when I'm just making it up.
  242. (Laughter)
  243. Thank you.
  244. (Applause)