Machine Generated Data
Tags
Amazon
created on 2022-01-23
Car | 99.8 | |
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Vehicle | 99.8 | |
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Automobile | 99.8 | |
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Transportation | 99.8 | |
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Person | 98.8 | |
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Human | 98.8 | |
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Car | 96.3 | |
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Car | 93.2 | |
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Person | 85.9 | |
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Sports Car | 74.7 | |
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Wheel | 73.3 | |
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Machine | 73.3 | |
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Person | 72.4 | |
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Coupe | 62.1 | |
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Sedan | 62 | |
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Tarmac | 60 | |
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Asphalt | 60 | |
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Home Decor | 55.6 | |
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Antique Car | 55.5 | |
|
Clarifai
created on 2023-10-26
car | 99.9 | |
| ||
vehicle | 99.8 | |
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transportation system | 99.5 | |
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people | 98.9 | |
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retro | 97.4 | |
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monochrome | 97.3 | |
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automotive | 94.5 | |
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classic | 94.4 | |
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street | 94 | |
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vintage | 93.6 | |
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no person | 91.7 | |
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convertible | 90 | |
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nostalgia | 89.2 | |
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adult | 89 | |
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road | 87.9 | |
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driver | 87.3 | |
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man | 86.1 | |
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drive | 83.8 | |
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travel | 83.1 | |
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hood | 82.9 | |
|
Imagga
created on 2022-01-23
car | 100 | |
| ||
motor vehicle | 45.4 | |
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transportation | 44.8 | |
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vehicle | 39.9 | |
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auto | 37.3 | |
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automobile | 34.4 | |
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transport | 33.8 | |
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cab | 27.6 | |
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drive | 24.6 | |
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wheel | 23.7 | |
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speed | 22.9 | |
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road | 20.8 | |
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travel | 19 | |
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truck | 18.7 | |
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motor | 18.1 | |
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power | 17.6 | |
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old | 17.4 | |
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metal | 16.9 | |
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wheeled vehicle | 16.5 | |
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luxury | 16.3 | |
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wheels | 15.6 | |
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fast | 14.9 | |
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vintage | 14.9 | |
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street | 14.7 | |
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cars | 14.6 | |
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style | 14.1 | |
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sports | 13.9 | |
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tire | 13.6 | |
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sedan | 13.2 | |
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classic | 13 | |
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headlight | 12.9 | |
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modern | 12.6 | |
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driving | 12.6 | |
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beach wagon | 12.5 | |
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expensive | 12.4 | |
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antique | 12.4 | |
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bumper | 12.1 | |
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light | 12 | |
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design | 11.8 | |
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device | 11.6 | |
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seat | 11.2 | |
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toaster | 11.2 | |
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retro | 10.6 | |
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rusty | 10.5 | |
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chrome | 10.4 | |
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shiny | 10.3 | |
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grille | 10.1 | |
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automotive | 9.8 | |
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engine | 9.6 | |
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sky | 9.6 | |
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new | 8.9 | |
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parked | 8.9 | |
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kitchen appliance | 8.6 | |
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model | 8.5 | |
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back | 8.3 | |
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tow truck | 8 | |
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machine | 8 | |
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business | 7.9 | |
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tires | 7.9 | |
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black | 7.8 | |
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abandoned | 7.8 | |
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windows | 7.7 | |
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door | 7.6 | |
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traffic | 7.6 | |
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support | 7.5 | |
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van | 7.3 | |
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grate | 7.3 | |
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aged | 7.2 | |
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silver | 7.1 | |
|
Google
created on 2022-01-23
Car | 96.3 | |
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Vehicle registration plate | 94.9 | |
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Vehicle | 94.5 | |
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Automotive lighting | 91.9 | |
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Motor vehicle | 88.5 | |
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Automotive design | 86.5 | |
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Automotive exterior | 79.8 | |
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Tints and shades | 76.1 | |
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Classic car | 75.5 | |
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Hardtop | 75.4 | |
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Classic | 75.2 | |
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Monochrome | 75.1 | |
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Kit car | 74.9 | |
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Monochrome photography | 74.6 | |
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Hood | 73.3 | |
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Bumper | 71.3 | |
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Personal luxury car | 69.2 | |
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Antique car | 69.2 | |
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Headlamp | 69.2 | |
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Family car | 68.9 | |
|
Microsoft
created on 2022-01-23
text | 99.2 | |
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vehicle | 94.2 | |
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land vehicle | 94 | |
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black and white | 90.8 | |
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car | 90.6 | |
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wheel | 74.4 | |
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old | 74.2 | |
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white | 69.9 | |
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transport | 67.4 | |
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black | 67 | |
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monochrome | 51 | |
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vintage | 36.7 | |
|
Color Analysis
Face analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20222016/878,177,33,41/full/0/native.jpg)
AWS Rekognition
Age | 49-57 |
Gender | Male, 80.6% |
Calm | 91.5% |
Sad | 6.9% |
Happy | 0.6% |
Confused | 0.5% |
Surprised | 0.2% |
Disgusted | 0.1% |
Angry | 0.1% |
Fear | 0% |
Feature analysis
Categories
Imagga
streetview architecture | 75.7% | |
| ||
paintings art | 11.1% | |
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interior objects | 6.7% | |
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cars vehicles | 3.8% | |
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text visuals | 1.5% | |
|
Captions
Microsoft
created on 2022-01-23
a vintage photo of a car | 85% | |
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a vintage photo of a truck | 84.9% | |
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a vintage photo of a person in a car | 77.9% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20222016/344,465,78,31/full/0/native.jpg)
JUST
![](https://ids.lib.harvard.edu/ids/iiif/20222016/468,510,43,24/full/0/native.jpg)
SHE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/363,505,148,28/full/0/native.jpg)
TONIGHT SHE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/460,534,76,30/full/0/native.jpg)
WORKS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/407,532,42,28/full/0/native.jpg)
THE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/405,430,67,26/full/0/native.jpg)
WATCH
![](https://ids.lib.harvard.edu/ids/iiif/20222016/344,465,198,36/full/0/native.jpg)
JUST MARRIED
![](https://ids.lib.harvard.edu/ids/iiif/20222016/363,505,100,27/full/0/native.jpg)
TONIGHT
![](https://ids.lib.harvard.edu/ids/iiif/20222016/413,382,61,15/full/0/native.jpg)
MISSOURI
![](https://ids.lib.harvard.edu/ids/iiif/20222016/435,469,106,33/full/0/native.jpg)
MARRIED
![](https://ids.lib.harvard.edu/ids/iiif/20222016/19,116,15,12/full/0/native.jpg)
BEN
![](https://ids.lib.harvard.edu/ids/iiif/20222016/494,412,44,23/full/0/native.jpg)
GOT
![](https://ids.lib.harvard.edu/ids/iiif/20222016/385,380,90,16/full/0/native.jpg)
SEP MISSOURI
![](https://ids.lib.harvard.edu/ids/iiif/20222016/341,529,59,30/full/0/native.jpg)
GETS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/385,381,24,13/full/0/native.jpg)
SEP
![](https://ids.lib.harvard.edu/ids/iiif/20222016/993,668,13,13/full/0/native.jpg)
4
![](https://ids.lib.harvard.edu/ids/iiif/20222016/373,431,11,18/full/0/native.jpg)
A
![](https://ids.lib.harvard.edu/ids/iiif/20222016/29,158,39,11/full/0/native.jpg)
SERVICE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/28,144,35,8/full/0/native.jpg)
PARTS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/370,403,68,27/full/0/native.jpg)
XMAS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/372,429,164,30/full/0/native.jpg)
A WATCH CASE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/19,115,52,13/full/0/native.jpg)
BEN STEPMAN
![](https://ids.lib.harvard.edu/ids/iiif/20222016/34,115,38,13/full/0/native.jpg)
STEPMAN
![](https://ids.lib.harvard.edu/ids/iiif/20222016/26,132,25,8/full/0/native.jpg)
MOTOR
![](https://ids.lib.harvard.edu/ids/iiif/20222016/484,435,52,23/full/0/native.jpg)
CASE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/340,529,196,42/full/0/native.jpg)
GETS THE CHE WORKS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/370,395,169,39/full/0/native.jpg)
XMAS S SHE GOT
![](https://ids.lib.harvard.edu/ids/iiif/20222016/26,131,42,9/full/0/native.jpg)
MOTOR CO.
![](https://ids.lib.harvard.edu/ids/iiif/20222016/872,116,51,23/full/0/native.jpg)
QUIETZA
![](https://ids.lib.harvard.edu/ids/iiif/20222016/53,131,16,9/full/0/native.jpg)
CO.
![](https://ids.lib.harvard.edu/ids/iiif/20222016/991,524,16,58/full/0/native.jpg)
ниссо
![](https://ids.lib.harvard.edu/ids/iiif/20222016/989,397,18,185/full/0/native.jpg)
ниссо ٤١٢W
![](https://ids.lib.harvard.edu/ids/iiif/20222016/990,397,14,40/full/0/native.jpg)
٤١٢W
![](https://ids.lib.harvard.edu/ids/iiif/20222016/41,153,13,5/full/0/native.jpg)
and
![](https://ids.lib.harvard.edu/ids/iiif/20222016/412,558,33,9/full/0/native.jpg)
CHE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/402,400,32,8/full/0/native.jpg)
S
![](https://ids.lib.harvard.edu/ids/iiif/20222016/81,779,262,44/full/0/native.jpg)
1111558
![](https://ids.lib.harvard.edu/ids/iiif/20222016/463,560,64,11/full/0/native.jpg)
MOICE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/15,116,1000,461/full/0/native.jpg)
BEN STEPMAN
MOTOR CO
aUIETZ
PARTS
SERVICE
SEP MISSOURI
XMAS SHE GOT
A WATCH CASE
JUST MARRIED
TONIGHT SHE
GETS THE WORKS
MJIR YT3RA2 002
![](https://ids.lib.harvard.edu/ids/iiif/20222016/15,117,24,19/full/0/native.jpg)
BEN
![](https://ids.lib.harvard.edu/ids/iiif/20222016/37,116,39,18/full/0/native.jpg)
STEPMAN
![](https://ids.lib.harvard.edu/ids/iiif/20222016/28,131,29,15/full/0/native.jpg)
MOTOR
![](https://ids.lib.harvard.edu/ids/iiif/20222016/55,133,16,12/full/0/native.jpg)
CO
![](https://ids.lib.harvard.edu/ids/iiif/20222016/873,120,51,21/full/0/native.jpg)
aUIETZ
![](https://ids.lib.harvard.edu/ids/iiif/20222016/30,145,38,12/full/0/native.jpg)
PARTS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/25,156,47,18/full/0/native.jpg)
SERVICE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/390,380,28,20/full/0/native.jpg)
SEP
![](https://ids.lib.harvard.edu/ids/iiif/20222016/419,381,67,21/full/0/native.jpg)
MISSOURI
![](https://ids.lib.harvard.edu/ids/iiif/20222016/370,399,80,40/full/0/native.jpg)
XMAS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/450,405,50,37/full/0/native.jpg)
SHE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/501,408,54,10/full/0/native.jpg)
GOT
![](https://ids.lib.harvard.edu/ids/iiif/20222016/376,437,14,16/full/0/native.jpg)
A
![](https://ids.lib.harvard.edu/ids/iiif/20222016/408,433,69,28/full/0/native.jpg)
WATCH
![](https://ids.lib.harvard.edu/ids/iiif/20222016/487,437,53,25/full/0/native.jpg)
CASE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/347,468,80,31/full/0/native.jpg)
JUST
![](https://ids.lib.harvard.edu/ids/iiif/20222016/440,473,106,32/full/0/native.jpg)
MARRIED
![](https://ids.lib.harvard.edu/ids/iiif/20222016/366,508,101,27/full/0/native.jpg)
TONIGHT
![](https://ids.lib.harvard.edu/ids/iiif/20222016/339,529,73,44/full/0/native.jpg)
GETS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/409,531,54,43/full/0/native.jpg)
THE
![](https://ids.lib.harvard.edu/ids/iiif/20222016/463,536,87,41/full/0/native.jpg)
WORKS
![](https://ids.lib.harvard.edu/ids/iiif/20222016/988,393,27,60/full/0/native.jpg)
MJIR
![](https://ids.lib.harvard.edu/ids/iiif/20222016/988,450,28,72/full/0/native.jpg)
YT3RA2
![](https://ids.lib.harvard.edu/ids/iiif/20222016/989,531,27,39/full/0/native.jpg)
002