The ABCs of Death

Race	White	Black	Latino	Jewish	Total	Visuals	Info
Kairos	4	4	1		9		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.kairos.com/demos
Forebears	3		1		4
Surname	2				2		A third party free program that uses US Census data to create an algorithm that analyzes surnames and guesses what ethnicity the person is. Obviously there are issues with this data set such as African Americans who have the last names of their ancestors' slave owner. However, when no other data set is available, this can give a rough estimate and expand the data pool for the less important cast members with no IMDb or TMDb image. Please consider downloading their source code via GitHub to verify the accuracy of our data set, and to peer review our conclusions. Source: https://pypi.org/project/ethnicolr/
Ethnic				1	1		This is the most reliable data source for accurate ethnic breakdowns of famous people. Often the information is crowdsourced by users and they cite all their sources (family trees, obituaries, census archives, etc.) on the bottom of the page. The only issue is converting their ambiguous and inconsistent verdicts into quantifiable variables. Source: https://ethnicelebs.com/
Crowd	1				1		This is the crowdsourced dataset created by users like you! We greatly appreciate your cooperation in making ZR the most accurate and massive database on the internet. Sometimes the verdicts are submitted by looks alone, sometimes users attach an image and a source link. Due to this inconsistency please take the results with a grain of salt and click each actor for more info.
Bettaface	1				1		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.betafaceapi.com/demo_old.html
Total	11	4	2	1	18
Cast Percentages	61.11%	22.22%	11.11%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":61.11,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":22.22,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Latino","y":11.11,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":5.56,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	57.84%	12.4%	18.73%	2.02%		{"name":"U.S. Population (2020)","series":[{"name":"White","y":57.84,"color":"#2b908f"},{"name":"Arab","y":0.54,"color":"#DF5353"},{"name":"Asian","y":6,"color":"#90ee7e"},{"name":"Black","y":12.4,"color":"#f45b5b"},{"name":"Indian","y":1.35,"color":"#7798BF"},{"name":"Indigenous","y":1.12,"color":"#aaeeee"},{"name":"Latino","y":18.73,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":9.32,"color":"#55BF3B"},{"name":"Jewish","y":2.02,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (2020) Representation	+3.27	+9.82	-7.62	+3.54		{"name":"U.S. Population (2020)","series":[["White",57.84],["Black",12.4],["Latino",18.73],["Jewish",2.02]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
U.S. Buying Power (2023) Percentage	72.69%	8.02%	10.06%			{"name":"U.S. Buying Power (2023)","series":[{"name":"White","y":13897000000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1333100000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1533100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":136400000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1924000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":295300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2023) Representation	-11.58	+14.2	+1.05			{"name":"U.S. Buying Power (2023)","series":[["White",72.69],["Black",8.02],["Latino",10.06],["Jewish",0]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
U.S. Average Individual Income (2018) Percentage	65.34%	9.43%	9.51%			{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	-4.23	+12.79	+1.6			{"name":"U.S. Average Individual Income (2018)","series":[["White",65.34],["Black",9.43],["Latino",9.51],["Jewish",0]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
World population (2019) Percentage	14.24%	14.47%	5.62%	0.34%		{"name":"World population (2019) ","series":[{"name":"Mixed \/ Other","y":14688882870,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":42991259469,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":10971786583,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":108889096410,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":110601821551,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":238860161621,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":67406299429,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":167470202093,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":2568698093,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World population (2019) Representation	+46.87	+7.75	+5.49	+5.22		{"name":"World population (2019) ","series":[["White",14.24],["Black",14.47],["Latino",5.62],["Jewish",0.34]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
World Buying Power Percentage	36.48%	6.38%	9.35%	1.11%		{"name":"World Buying Power","series":[{"name":"White","y":3010602822170000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":771525888400000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":91700962050000,"color":"#ff0066","sliced":true,"selected":true},{"name":"Black","y":526264226360000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indigenous","y":174285727610000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":148984454560000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Arab","y":526400066100000,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":2431392083750000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Indian","y":572687873900000,"color":"#7798BF","sliced":true,"selected":true}],"prefix":"$"}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World Buying Power Representation	+24.63	+15.84	+1.76	+4.45		{"name":"World Buying Power","series":[["White",36.48],["Black",6.38],["Latino",9.35],["Jewish",1.11]],"cast":[{"name":"White","y":61.11,"color":"#2b908f"},{"name":"Black","y":22.22,"color":"#f45b5b"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Jewish","y":5.56,"color":"#ff0066"}]}
Notes			Many Latinos check "White" or "Other" on the Census. So this overlaps with "Mixed" race and "White." Also, when there was a discrepancy with the percentage listed on the Wikipedia table and our calculated numbers, the Wiki table's numbers were used. Likewise, when the population total was missing from the Census pdf, the estimate was calculated using the percentage from the Wiki table. NOTE: It turns out that the reason America doesn't have a separate "Latino" race is because the League of Latin American Citizens protested against discrimination towards Mexicans and got themselves to legally be considered "White." Check the research links in our Google Doc for more info.	If there were any inconsistencies with percentages calculated using our Census data, the Jewish Data Bank percentages were the ones used. Also please review the Jewish Data Bank pdf to understand the difference between the "Core" Jewish population and the "Extended" / "Law of Return" Jewish populations. We are displaying the "Core" Jewish populations since that is what the Jewish Data Bank and Wikipedia articles chose. Also, the 1942 population estimates were used for the 1940 row. For the 2000 estimates, the "Core" definition was not used, so we used the 5.2 mil broader estimate instead of the 4.3 mil estimate since the 5.2 broader estimate seemed too drastic of a drop and the definitions weren't congruent with Core and Law of Return.

555 phone number abominable snowman character accidental death animal attack animal bite animal cruelty animated sequence anthology apocalypse attack dog attacked with a knife auditory hallucination bare chested male bare chested male bondage beach bikini bird in a cage biting an animal bitten in the neck bitten on the arm bitten on the leg black leather glove blood blood on camera lens blood splatter bound and gagged bow and arrow bow hunting bra removing breasts brutality burn scar camera focus on female butt car chase carnage carrot cartoon cartoon nudity chainsaw character repeats someone else's dialogue child comforts mother child molestation claymation cleaved hand close up of eye clown cocaine contrail controversy counting sheep cousin cousin relationship covered in blood crushed head crying baby cult film death death in title death of child death of protagonist decapitated child decapitation deer desert disturbing dog dog costume dog tag doppelganger doppleganger dripping faucet drug addict drug overdose duck earthquake electric knife electrocution embarrassing male nudity embarrassing nudity excrement execution exploding head explosion eye gouged out f rated fart farting farting in someone's face fat woman female frontal nudity female masturbation female orgasm female rear nudity firefight flamethrower flashback flatulence forced to masturbate forest french funny face girl farting gore hallucination hara kiri head ripped off held at gunpoint hit with a fire extinguisher hit with a frying pan hit with a shovel hole in chest human versus animal husband murders wife impalement infanticide infertility infidelity injection intravenous japan japanese japanese schoolgirl katana katana sword killed during sex killing an animal kitten knife large penis leg hold trap lens flare lesbian lesbian kiss libido locked in a car trunk male masturbation male nudity male objectification man sits on a toilet man vomits marriage proposal masturbation masturbation competition men masturbate together mexico city middle school mind control miniskirt miscarriage misfire monster mother killed murder murder of a child murdered with a chainsaw murdered with an axe news report nightmare nude fight nudity obesity omnibus one day timespan one night timespan onion parrot part animated pedophile person on fire perversion photographic film pistol plugged toilet plunger point of view poisoned priest prosthetic leg prostitute psychic psychotronic film public masturbation public nudity punched in the crotch red and black spider reference to elvis presley reference to kunta kinte reflection in a pool of blood robot robot cop sashimi scalded face scratching screaming woman self cutting self mutilation semen seppuku sequel mentioned during end credits severed head severed tongue sex standing up shooting up shootout shot in the chest shot in the head shot to death single mother skinned alive slow motion scene snorting cocaine with a dollar bill soap bubble social masturbation spider spider bite split screen stabbed in the chest stabbed in the crotch stabbed in the eye stabbed in the hand stabbed in the head stabbed in the leg stabbed in the neck stabbed in the stomach stabbed in the throat stabbed repeatedly stabbed through the chin stabbed to death stag head stepping in feces stepping in shit stomping on a kitten stop motion storytelling strangled to death strangulation strapped to a chair striptease subjective camera subtitled scene subway station suicide surfboard surfer surrealism swastika swimming underwater syringe talking parrot teacher student relationship teasing to suicide thrown through a window thunderstorm tied to a bed tied to a chair time loop title at the end toddle wearing a diaper toilet tooth pulling toxic gas underwater scene vampire vancouver british columbia canada visual hallucination walrus watching someone masturbate wheelchair white duck wife murders husband winston churchill character woman sits on a toilet woman vomits woman wears a micro miniskirt woman wears a neck brace woman wears black lingerie wooden stake written by director year 2035 yellow tabby kitten zombie zombie clown

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The ABCs of Death

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