Magical Girl

Summary: The father of a terminally ill girl is determined to grant his daughter's last wish, a ridiculously expensive collectors' costume from a Japanese TV series. The request will drive him into a journey within his city's dark underbelly.

Release Date: October 17, 2014

Genres: Drama, Thriller

Director: Carlos Vermut

Writer: Carlos Vermut

Runtime: 2 h 07 min

Production: Aquí y Allí Films, Televisión Española (TVE), Canal+ España, Sabre Producciones, Lovemonk

Countries: Spain, France

Language: Spanish

Box office:

Worldwide:$213.8K

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56%73%

56% of the Stars & Main Cast are Female.

73% of the Stars & Main Cast are "diverse."

Latino72.73%White27.27%

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CONTENT BREAKDOWN

Methodology

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Methodology

Cast

Stars:

Supporting cast:

Other Cast, Production

Other cast:

Production:

Representation

Star

Supporting

Other

Production

Race	White	Asian	Mixed / Other	Total	Visuals	Info
Forebears	9			9
Surname	3			3		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	2		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/
Kairos	2			2		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
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
Familysearch	1			1
Total	16	1	1	18
Cast Percentages	88.89%	5.56%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":88.89,"color":"#2b908f","sliced":true,"selected":true},{"name":"Asian","y":5.56,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B","sliced":true,"selected":true}],"prefix":""}
U.S. Population (1990) Percentage	75.6%	2.78%	3.9%		{"name":"U.S. Population (1990)","series":[{"name":"White","y":75.6,"color":"#2b908f"},{"name":"Arab","y":0.35,"color":"#DF5353"},{"name":"Asian","y":2.78,"color":"#90ee7e"},{"name":"Black","y":12.1,"color":"#f45b5b"},{"name":"Indian","y":0.33,"color":"#7798BF"},{"name":"Indigenous","y":0.8,"color":"#aaeeee"},{"name":"Latino","y":9,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":3.9,"color":"#55BF3B"},{"name":"Jewish","y":2.22,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (1990) Representation	+13.29	+2.78	+1.66		{"name":"U.S. Population (1990)","series":[["White",75.6],["Asian",2.78],["Mixed \/ Other",3.9]],"cast":[{"name":"White","y":88.89,"color":"#2b908f"},{"name":"Asian","y":5.56,"color":"#90ee7e"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
U.S. Buying Power (2000) Percentage	81.25%	3.5%	0.76%		{"name":"U.S. Buying Power (2000)","series":[{"name":"White","y":6413300000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":276400000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":609100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":40300000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":494000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":60300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2000) Representation	+7.64	+2.06	+4.8		{"name":"U.S. Buying Power (2000)","series":[["White",81.25],["Asian",3.5],["Mixed \/ Other",0.76]],"cast":[{"name":"White","y":88.89,"color":"#2b908f"},{"name":"Asian","y":5.56,"color":"#90ee7e"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
U.S. Average Individual Income (2018) Percentage	80.3%	3.28%	2.87%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":12078730400895,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":492694654404,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1301541039245,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":91710978434,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":646415280000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":431114358324,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	+8.59	+2.28	+2.69		{"name":"U.S. Average Individual Income (2018)","series":[["White",80.3],["Asian",3.28],["Mixed \/ Other",2.87]],"cast":[{"name":"White","y":88.89,"color":"#2b908f"},{"name":"Asian","y":5.56,"color":"#90ee7e"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
World population (1992) Percentage	17.84%	35%	1.95%		{"name":"World population (1992) ","series":[{"name":"Mixed \/ Other","y":10596121575,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":30821675401,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":7193470722,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":96878442458,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":57125167938,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":190026935420,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":38911036281,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":109563210222,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":1843199181,"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 (1992) Representation	+71.05	-29.44	+3.61		{"name":"World population (1992) ","series":[["White",17.84],["Asian",35],["Mixed \/ Other",1.95]],"cast":[{"name":"White","y":88.89,"color":"#2b908f"},{"name":"Asian","y":5.56,"color":"#90ee7e"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"}]}
World Buying Power Percentage	36.48%	29.46%	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	+52.41	-23.9	+3.75		{"name":"World Buying Power","series":[["White",36.48],["Asian",29.46],["Mixed \/ Other",1.81]],"cast":[{"name":"White","y":88.89,"color":"#2b908f"},{"name":"Asian","y":5.56,"color":"#90ee7e"},{"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.	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.

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This movie passed 3 of 3 tests. It was entered by Chacón on 2015-03-11 16:35:12

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Magical Girl

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