Clandestine Childhood

Summary: Juan lives in clandestinity. Just like his mum, his dad and his adored uncle Beto, outside his home he has another name. At school, Juan is known as Ernesto. And he meets María, who only has one name. Based on true facts, set in t...

Release Date: September 20, 2012

Genre: Drama

Director: Benjamín Ávila

Writers: Benjamín Ávila

, Marcelo Müller

Runtime: 1 h 52 min

MPAA: Not Rated

Production: Historias Cinematograficas, Habitacion 1520 Producciones, RTA Radio y Televisión Argentina, Programa Ibermedia, Televisión Española (TVE), Ancine, Instituto de la Cinematografía y de las Artes Audiovisuales (ICAA), Instituto Nacional de Cine y Artes Audiovisuales (INCAA), TV Publica Digital

Distributor: Film Movement, SOMOS Next

Countries: Argentina, Spain, Brazil

Language: Spanish

Movie budget: $ 1,009,653

Domestic: $ 9,017

International: $ 1,203,337

Box office:

Budget:$1MDomestic:$9KInternational:$1.2MWorldwide:$1.2M

Box office International:

Source: The Numbers

Adaptation/remake:

based on true story

24%

The Rotten Tomatoes audience rated this 24% higher than the critics.

45%69%

No Metacritic ratings imported yet.

44%53%

44% of the Stars & Main Cast are Female.

53% of the Stars & Main Cast are "diverse."

Latino46.67%White46.67%Mixed / Other6.67%

N/A

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CONTENT BREAKDOWN

Methodology

RottenTomatoes:

45%69%

Douban:

IMDb:

Kinopoisk:

Moviemeter:

OFDb:

Methodology

Cast

Stars:

Supporting cast:

Other Cast, Production

Other cast:

Production:

Representation

Star

Supporting

Other

Production

Race	Asian	White	Black	Latino	Mixed / Other	Total	Visuals	Info
Kairos	6	2	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
Ethnic	1	1	1		1	4		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/
Forebears	2	1				3
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
Surname		1				1		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/
Total	9	5	2	1	1	18
Cast Percentages	50%	27.78%	11.11%	5.56%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"Asian","y":50,"color":"#90ee7e","sliced":true,"selected":true},{"name":"White","y":27.78,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":11.11,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Latino","y":5.56,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	6%	57.84%	12.4%	18.73%	9.32%		{"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	+44	-30.06	-1.29	-13.17	-3.76		{"name":"U.S. Population (2020)","series":[["Asian",6],["White",57.84],["Black",12.4],["Latino",18.73],["Mixed \/ Other",9.32]],"cast":[{"name":"Asian","y":50,"color":"#90ee7e"},{"name":"White","y":27.78,"color":"#2b908f"},{"name":"Black","y":11.11,"color":"#f45b5b"},{"name":"Latino","y":5.56,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
U.S. Buying Power (2023) Percentage	6.97%	72.69%	8.02%	10.06%	1.54%		{"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":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2023) Representation	+43.03	-44.91	+3.09	-4.5	+4.02		{"name":"U.S. Buying Power (2023)","series":[["Asian",6.97],["White",72.69],["Black",8.02],["Latino",10.06],["Mixed \/ Other",1.54]],"cast":[{"name":"Asian","y":50,"color":"#90ee7e"},{"name":"White","y":27.78,"color":"#2b908f"},{"name":"Black","y":11.11,"color":"#f45b5b"},{"name":"Latino","y":5.56,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
U.S. Average Individual Income (2018) Percentage	7.52%	65.34%	9.43%	9.51%	7.28%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	+42.48	-37.56	+1.68	-3.95	-1.72		{"name":"U.S. Average Individual Income (2018)","series":[["Asian",7.52],["White",65.34],["Black",9.43],["Latino",9.51],["Mixed \/ Other",7.28]],"cast":[{"name":"Asian","y":50,"color":"#90ee7e"},{"name":"White","y":27.78,"color":"#2b908f"},{"name":"Black","y":11.11,"color":"#f45b5b"},{"name":"Latino","y":5.56,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
World population (2019) Percentage	31.25%	14.24%	14.47%	5.62%	1.92%		{"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	+18.75	+13.54	-3.36	-0.06	+3.64		{"name":"World population (2019) ","series":[["Asian",31.25],["White",14.24],["Black",14.47],["Latino",5.62],["Mixed \/ Other",1.92]],"cast":[{"name":"Asian","y":50,"color":"#90ee7e"},{"name":"White","y":27.78,"color":"#2b908f"},{"name":"Black","y":11.11,"color":"#f45b5b"},{"name":"Latino","y":5.56,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
World Buying Power Percentage	29.46%	36.48%	6.38%	9.35%	1.81%		{"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	+20.54	-8.7	+4.73	-3.79	+3.75		{"name":"World Buying Power","series":[["Asian",29.46],["White",36.48],["Black",6.38],["Latino",9.35],["Mixed \/ Other",1.81]],"cast":[{"name":"Asian","y":50,"color":"#90ee7e"},{"name":"White","y":27.78,"color":"#2b908f"},{"name":"Black","y":11.11,"color":"#f45b5b"},{"name":"Latino","y":5.56,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
Notes	The US Census considers Pakistani, Sri Lankan, Indian, and Bangladeshi as Asians. At ZR we group Pakistanis with the Arab world. And we group Sri Lankan and Bangladeshi with India because 1.) they are all genetically similar as "South Asians and 2.) there are only a handful of actors from those regions in our database. However, you are viewing the "Asian Alone" category from the US Census when comparing it with Domestic representation. Also note that the 2000 and 2010 estimates include MultiRacial Asians not just "Asian Alone" estimates, and in some categories they include Native Hawaiians as "Asian." So there is a margin of error less than 1% and some inconsistencies with the grouping.			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.	The "Mixed / Other" categories overlap, and the 2 or more category was not an option until the 2000 Census. So for the combined column, all data will be taken from the "Other" column except for 2000, 2010, & 2020. For those, the average between the "2 or More Races" and "Some Other Race" will be used.