T E T R A H E D R O N Johnny Bedini BED12368924
T
E R
D A
H O N
Contents
05 start one: gci one: whr
one: gnp
22 28
conclusions 50
two: ics 38
003
14
I decided to take a generative apprach to choosing my three words.
First, though, I had to set some parameters.
004
I would take my three words from three broad categories; noun, verb, and adjective.
All I had to do then was populate each category then generate a choice from each.
ambition poet college drama assistant concept competition moment historian statement county atmosphere loss guitar president apartment director replacement education uncle chemistry tongue family discussion idea birthday method marriage fishing studio person permission
government assignment hat proposal theory lady imagination vehicle dirt tale cancer literature teaching writing advertising storage mixture discussion breath exam imagination manager client ad responsibility apple writer inspector reflection moment lotion independence sector safety
introduction hat efficiency finding elevator child wife country percentage physics relation performance birthday perception hall confusion midnight lady wood nation maintenance session soup role guidance disaster diamond growth boyfriend politics coffee payment refrigerator cabinet
005
Nouns
Verbs
006
touch saturate characterise contour incorporate render outline make intrigue commission communicate excite focus conjure draw pioneer experience contrast envision immerse remind connect interweave layer uplift recreate embody enchant explore create shape design
articulate evoke invert juxtapose emancipate conceptualise sketch interlace critique exhibit capture express transform manipulate illustrate feel brush fuse redefine reach emphasise shoot visualize view inspire hang depict carve study witness triangulate inform construct captivate
represent affect sculpt emerge fascinate display convey accomplish elevate speak awaken play refine compose arouse reflect show reveal portray impassion define apply distort photograph paint interpret embellish decorate etch dance develop stir echo enhance
curious wooden hungry nice popular efficient existing famous terrible substantial reasonable weak odd powerful capable pregnant entire old successfully angry remarkable healthy united suitable strict latter dramatic every pure logical automatic friendly
massive aggressive severe educational various conscious distinct environmental unhappy historical impressive scared emotional used eastern unable huge legal cute informal guilty alive electrical sudden intelligent acceptable available electronic recent suspicious additional tall political helpful
impossible mental confident realistic unusual lucky willing useful actual global foreign civil basic known federal psychological inner competitive difficult accurate exciting technical sufficient pleasant dangerous serious embarrassed boring immediate different relevant ugly numerous important
007
Adjectives
I then used a random number generator (random.org) to choose my words.
The number would then correspond to the number (out of 100) of a word. 008
I then repeated this for the other two categories, resulting in my assigned choices.
The results were as follows...
My first result was 23, which gave me the noun family.
23
85
The second was 85, which provided the verb impassion.
VERB
The final number to be generated was 78, resulting in civil.
78 ADJECTIVE
009
NOUN
civil
family
0010
? ? ? impassion
Therefore I decided to expand each word through synonyms and word association into a list which would then help me to find these hidden connections.
0011
Whilst it may look like there are no connections at all between these three words, my purpose for doing this generation process was to force the connections.
Family
Impassion
Civil
Group Support Type Unit Brother Sister Father Mother Parents Siblings Cousins Kin Tribe Connected Blood Member Friends Friendly Uncle Aunt Husband Wife Marriage Tree Descendents Relative Relation House Clan Home
Excite Entice Encourage Fervour Intense Rouse Vehement Zealous Feverish Superheat Warm Frenzy Implore Convince Persuade Polarise Lead Emotion Heartfelt Earnest Burn Intensify Energise Animate Fanatic(ise) Radicalise Enchant
Law Friendly Polite Civilisation Political Institution Government Organisation House (of Commons/ Lords) Partnership Marriage Civilian Domestic National State Local Home Public Social Community Municipal Courteous Gracious Urbane Cultured Pleasant Genial Secular
NOUN
VERB
ADJ.
0012
N.B. Not all the words in the lists are of the same category as the word from which they are derived. For example, ‘earnest’ is an adjective, but is related to impassion, a verb.
0013
triangulation no. 1
This list provided many interesting connections, many involving politics. My first exploration involved just that, focusing on the intersection of brother, heartfelt and national. The first case study was the Good Country Index.
0014
The Good Country Index
Note: ‘goodness’, as defined by Simon Anholt, is not good in the sense of better or best, but ‘gooder’ and ‘goodest’. “This is a country which simply gives more to humanity than any other country. I don’t talk about how they behave at home because that’s measured elsewhere.” - Simon Anholt
0015
±
The Good Country Index is a global ranking system which rates countries in terms of the ‘goodness’ that they do outside of their own country. Launched in 2014 by Simon Anholt, the GCI uses seven categories in order to calculate its overall ranking:
0016
Science & Technology Culture International Peace & Security World Order Planet & Climate Prosperity & Equality Health & Wellbeing The results are equally unexpected and interesting the UK makes a respectable 7th, mostly due to our worldleading science and technology exports, but I’d be very suprised if you could guess more than two of the top five...
1. 2. 3. 4. 5.
0017
The ‘Goodest’
0018
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.
Ireland Finland Switzerland Netherlands New Zealand Sweden United Kingdom Norway Denmark Belgium France Canada Germany Austria Australia Luxembourg Iceland Cyprus Spain Italy United States Costa Rica Malta Chile Japan Kenya Singapore Slovenia Guatemala
30. 31. 32. 33.
Greece Columbia Bulgaria Panama
34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62.
Estonia Portugal Mauritius Israel Ghana Ecuador Zambia Uruguay Slovakia Czech Republic South Africa Jamaica Croatia South Korea Namibia Brazil Jordan Trinidad & Tobago Poland Thailand Paraguay Macedonia Tunisia Argentina Malaysia Uganda Moldova Hungary Serbia
Tanzania Botswana Romania Mexico
96. 97. 98. 99.
D.R. Congo Togo Madagascar Ukraine
67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95.
Morocco Egypt Lithuania Bosnia & Herz. Mozambique Armenia Albania Kyrgyzstan Malawi Lesotho Georgia Sri Lanka Turkey Kazakhstan India Belarus Latvia Lebanon El Salvador Peru U. A. E. Bolivia Cameroon Senegal Bangladesh Saudia Arabia Kuwait Honduras Russia
100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
Oman Dominican Republic Congo Nigeria Laos Sudan Pakistan China Rwanda Mongolia Qatar Algeria Cambodia Syria Phillipines Iran Yemen Venezuela Benin Indonesia Zimbabwe Angola Azerbaijan Iraq Vietnam Libya
The Least Good
0019
63. 64. 65. 66.
Breakdown by Continent
0020
Europe Oceania North America South America Asia Africa
0021
Interestingly, each major continent (Oceania are excluded from this graphic as they only have two countries included in the study) is grouped rather conveniently, with their range from highest to lowest scoring country being the same as their continental score. I.e. Europe has both the top scoring country (Ireland), and the least low scoring bottom country (Ukraine).
Europe
North America
South America
Asia
Africa
0022
“ Those who are happiest are those who do the most for others ” BOOKER T WASHINGTON
0023 I’ve chosen Afro-American Civil Rights leader Booker T. Washington’s quote regarding the benefits of altruism, but it is a commonly held belief that doing good to others feels good. I wanted to test this theory in a slightly tounge-in-cheek manner by comparing the Good Country Index to the United Nations’ World Happiness Report. The theory would be justified if there was a positive correlation between the data sets - the more good a country does to the world in general should lead to its people being happier.
Goodness
0024 Happiness
The ‘best’ place for a country would be bottom left - both axes are measures of rank, and therefore lower is better.
0025
If this theory is correct, this is how the correlation between happiness and goodness would look. The X axis represents the UN Global Happiness Report score whilst the Y shows the Good Country Index rank.
X axis - Good Country Index Y axis - Global Happiness Report
0026
0027 As you can see, there was little parallel between the two. There is a slight positive correlation, particularly amongst the highest scorers for both categories (bottom left), but the middle and right sections provide little evidence for an interrelationship. The orange line, marking the outcome predicted on the previous page, hardly relates at all to the data around it, signifying the lack of correlation. It would seem that, from this evidence at least, doing good for others does not make you happier.
0028
“ Money has never made man happy, nor will it. The more of it one has, the more one wants ” BENJAMIN FRANKLIN
So having found that doing good doesn’t particularly affect happiness on a civic level, what other factors might? Much like Booker T. Washington’s quote regarding do-goodery, Franklin’s sentiment is one that is often repeated and reworded. It is viewed as a general fact of life that money cannot buy the more spiritual things in life - love and happiness being chief among them.
I again wanted to take this theory to task on a national level, comparing the UN’s Global Happiness Report with the Gross Domestic Product (at Purchasing Power Parity) per Capita of each country, using the Central Intelligence Agency’s World Factbook data (2014).
0029
£
GNP (PPP) per Capita
0030
Happiness
0031 Much like the previous graph, a positive correlation in this chart would show that, on a national level, the amount of money per person causes greater happiness. The ‘best’ place for a country would be bottom left - both axes are measures of rank, and therefore lower is better. Turn over to see the results...
0032
0033 In sad news for humanity, it is clear that there is more of a correlation here, between money and happiness, than there was between happiness and altruism. It is by no means a perfect correlation, but the spread of data is much less variable.
0034
0035 Key: x = Good Country Index & World Happiness Report x = GNP (PPP) Per Capita & World Happiness Report
Our investigation into brother, heartfelt and national has resulted in some interesting outcomes. Firstly that there is little correlation between country happiness and altruism, and secondly that there is significantly more of a relationship between wealth and happiness on a national scale. 0036
These outcomes should be taken with a pinch of salt of course - the metrics used for both happiness and ‘goodness’ are somewhat arbitrary, and the GNP PPP Per Capita of a country neither perfectly represents the wealth of its individual citizens nor the spread of wealth amongst them. However its time for change, so on to a new triangulation.
0037
triangulation no. 2
My next three words chosen from the source list were tribe, polarise and political. This was to be an investigation of the increasing polarisation of the average voter, and the factors surrounding that change of attitude.
0038
The Ideologica Consistenc Scale
↔ There is too much at stake for us to surrender to the politics of polarization. - Brad Henry, former Governor of Oklahoma
0039
al ncy
The Ideological Consistency Scale is a report by the U.S. Pew Research Center. It takes the form of a many questioned survey containing questions designed to reveal conservative or liberal leanings, such as: The government should do more to help needy Americans, even if it means going deeper into debt vs.
0040
The government today can’t afford to do much more to help the needy Choosing the first answer would give you a liberal point, whilst answering with the second response would earn a conservative one. As with any survey technique it isn’t perfect but the relative subtlety of the ICS, as well as the scoring mechanics, allow it to represent political consensus fairly accurately.
0041
1994 1999 2004 2011 2014 2015
0042 Consistently Liberal
Mostly Liberal
Mix
xed
It is often speculated that we live in an era of increased polarisation, be it in politics, music, art, or really any segment of society. A potential factor in this could be the ‘echo chamber’ effect of the internet, whereby someone with an opinion on something will surround themselves, or find themselves surrounded by, people of a similar opinion. Therefore that opinion is amplified by the people agreeing with it, causing the original opinion holder to become more radical in his thinking. First of all however, we will be exploring American politics as a metaphor for general polarisation, due to the large amount of data available (the UK, by comparison, seemingly has no statistics on ideological preference).
0043
This graph shows how ‘centrism’ has reduced since 1994, a decrease from 49% to 38%. Remarkably as late as 2004 49% of people had centrist views, before rapidly decreasing over the next eleven years.
Mostly Conservative
Consistently Conservative
2015 2014 2011 2004 1999 1994
0044 Consistently Liberal
Mostly Liberal
Mix
xed
This rearrangement of the same data showing 1994 in the foreground shows the other side to the story - the rise of the extremes. Since 1999 voters on the far right have increased from 4% to 10%, whilst consistently left leaning individuals have shot from 3% in 1994 to 13% in 2015. This is indicated by the bulges of dark colour on the left and right side of the diagram.
0045
This change has been recognised in the real world with the rise of extreme candidates - Donald Trump and Ted Cruz on the right and Bernie Sanders on the left. Even here in the UK the same phenomenon is apparent with the emergence of UKIP and the nomination of Jeremy Corbyn.
Mostly Conservative
Consistently Conservative
0046 Consistently Liberal
Mostly Liberal
Mix
xed
This bar chart shows very clearly the changes in each category.
0047
Both the extremes, the consistently conservatives and consistently liberals, show an increase in population over the timeframe, whilst the centrists and more moderate viewpoints lose out.
Mostly Conservative
Consistently Conservative
0048
16 38 Percent of Democrats in 1994 that viewed Republicans ‘extremely unfavourably’.
Percent of Democrats in 2015 that viewed Republicans ‘extremely unfavourably’.
Percent of Republican in 1994 that viewed Democrats ‘extremely unfavourably’.
Percent of Republican in 2015 that viewed Democrats ‘extremely unfavourably’.
0049
17 43
0050
Conclusion
0051
ns
This project was excruciating to do, but the result was very satisfying. The information was interesting, but it was the graphical potential of the data that really got me going. Whilst the 5-6 hours each graphic took in copying data, comparing it, and visualising it was arduous and unpleasant, the work was worth it in the end.
with my other two projects, both of which were quite un-Adobe.
Overall I wish I could have had more time to explore this project, as it would essentially be limitless. I am also disappointed, in retrospect, with how long it took me to settle on three words to explore. I probably would not have generated my words, if I was to redo This is a project that, this project, as, while it quite uniquely for me, was was interesting to explore almost entirely unaffected the forced connections by ‘design’. There was no between three random visual research, no real words, it would probably imagery, just the data have been easier to and the correlations and be motivated if I had shapes created by it. chosen three things that I like/am interested in. This project will hopefully be useful as But overall, I’m happy a demonstration of my with this project, and am digital skills in typography pleased with the direction and various software, it went in, as it managed especially in combination to play to my strengths.
T E T R A H E D R O N Johnny Bedini BED12368924