Cross Creek

Summary: In the 1930s, Marjorie Kinnan Rawlings moves to Florida's backwaters to write in peace. She feels bothered by affectionate men, editor and confused neighbors, but soon she connects and writes The Yearling, a classic of American li...

Release Date: October 27, 1983

Genres: Drama, Romance, Biography

Director: Martin Ritt

Writers: Marjorie Kinnan Rawlings

, Dalene Young

Runtime: 2 h 07 min

MPAA: PG

Production: Thorn EMI Screen Entertainment, Universal Pictures

Country: United States

Language: English

Domestic: $ 200,000

Box office:

Domestic:$200KWorldwide:$200K

Adaptation/remake:

based on true story, based on book, based on memoir

Possible Big Business:

Production:

Universal Pictures

18%

The Rotten Tomatoes audience rated this 18% higher than the critics.

56%74%

No Metacritic ratings imported yet.

33%6%

33% of the Stars & Main Cast are Female.

6% of the Stars & Main Cast are "diverse."

White87.5%Black6.25%Jewish6.25%

N/A

Possibly woke elements:

f rated

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CONTENT BREAKDOWN

Methodology

RottenTomatoes:

56%74%

Douban:

IMDb:

OFDb:

Methodology

Cast

Stars:

Supporting cast:

Other Cast, Production

Other cast:

Production:

Representation

Star

Supporting

Other

Production

Race	White	Mixed / Other	Asian	Latino	Black	Total	Visuals	Info
Kairos	5		1	1	1	8		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	2				3		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
Total	8	2	2	1	1	14
Cast Percentages	57.14%	14.29%	14.29%	7.14%	7.14%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":57.14,"color":"#2b908f","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Asian","y":14.29,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Latino","y":7.14,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Black","y":7.14,"color":"#f45b5b","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	57.84%	9.32%	6%	18.73%	12.4%		{"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	-0.7	+4.97	+8.29	-11.59	-5.26		{"name":"U.S. Population (2020)","series":[["White",57.84],["Mixed \/ Other",9.32],["Asian",6],["Latino",18.73],["Black",12.4]],"cast":[{"name":"White","y":57.14,"color":"#2b908f"},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B"},{"name":"Asian","y":14.29,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"},{"name":"Black","y":7.14,"color":"#f45b5b"}]}
U.S. Buying Power (2018) Percentage	74.35%	1.38%	6.2%	9.42%	7.96%		{"name":"U.S. Buying Power (2018)","series":[{"name":"White","y":12147700000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1013300000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1300200000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":114600000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1539000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":224700000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2018) Representation	-17.21	+12.91	+8.09	-2.28	-0.82		{"name":"U.S. Buying Power (2018)","series":[["White",74.35],["Mixed \/ Other",1.38],["Asian",6.2],["Latino",9.42],["Black",7.96]],"cast":[{"name":"White","y":57.14,"color":"#2b908f"},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B"},{"name":"Asian","y":14.29,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"},{"name":"Black","y":7.14,"color":"#f45b5b"}]}
U.S. Average Individual Income (2018) Percentage	65.34%	7.28%	7.52%	9.51%	9.43%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	-8.2	+7.01	+6.77	-2.37	-2.29		{"name":"U.S. Average Individual Income (2018)","series":[["White",65.34],["Mixed \/ Other",7.28],["Asian",7.52],["Latino",9.51],["Black",9.43]],"cast":[{"name":"White","y":57.14,"color":"#2b908f"},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B"},{"name":"Asian","y":14.29,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"},{"name":"Black","y":7.14,"color":"#f45b5b"}]}
World population (2019) Percentage	14.24%	1.92%	31.25%	5.62%	14.47%		{"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	+42.9	+12.37	-16.96	+1.52	-7.33		{"name":"World population (2019) ","series":[["White",14.24],["Mixed \/ Other",1.92],["Asian",31.25],["Latino",5.62],["Black",14.47]],"cast":[{"name":"White","y":57.14,"color":"#2b908f"},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B"},{"name":"Asian","y":14.29,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"},{"name":"Black","y":7.14,"color":"#f45b5b"}]}
World Buying Power Percentage	36.48%	1.81%	29.46%	9.35%	6.38%		{"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	+20.66	+12.48	-15.17	-2.21	+0.76		{"name":"World Buying Power","series":[["White",36.48],["Mixed \/ Other",1.81],["Asian",29.46],["Latino",9.35],["Black",6.38]],"cast":[{"name":"White","y":57.14,"color":"#2b908f"},{"name":"Mixed \/ Other","y":14.29,"color":"#55BF3B"},{"name":"Asian","y":14.29,"color":"#90ee7e"},{"name":"Latino","y":7.14,"color":"#eeaaee"},{"name":"Black","y":7.14,"color":"#f45b5b"}]}
Notes		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.	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.

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Cross Creek

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