ethically.we.data._extract_caliskan

Complexity

Total Complexity

2

Size/Duplication

Total Lines	200
Duplicated Lines	0 %

Importance

Changes

0

2 Functions

Rating	Name	Duplication	Size	Complexity
A	parse_case()	0	6	1
A	parse_line()	0	3	1

# pylint: skip-file

import json


TEXT = '''WEAT_DATA 1: We use the flower and insect target words along with pleasant and unpleasant attributes
found in (5).
• Flowers: aster, clover, hyacinth, marigold, poppy, azalea, crocus, iris, orchid, rose, bluebell,
daffodil, lilac, pansy, tulip, buttercup, daisy, lily, peony, violet, carnation, gladiola,
magnolia, petunia, zinnia.
• Insects: ant, caterpillar, flea, locust, spider, bedbug, centipede, fly, maggot, tarantula,
bee, cockroach, gnat, mosquito, termite, beetle, cricket, hornet, moth, wasp, blackfly,
dragonfly, horsefly, roach, weevil.
• Pleasant: caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond,
gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family,
happy, laughter, paradise, vacation.
• Unpleasant: abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink,
assault, disaster, hatred, pollute, tragedy, divorce, jail, poverty, ugly, cancer, kill, rotten,
vomit, agony, prison.
WEAT_DATA 2: We use the musical instruments and weapons target words along with pleasant and
unpleasant attributes found in (5).
• Instruments: bagpipe, cello, guitar, lute, trombone, banjo, clarinet, harmonica, mandolin,
trumpet, bassoon, drum, harp, oboe, tuba, bell, fiddle, harpsichord, piano, viola, bongo,
flute, horn, saxophone, violin.
• Weapons: arrow, club, gun, missile, spear, axe, dagger, harpoon, pistol, sword, blade,
dynamite, hatchet, rifle, tank, bomb, firearm, knife, shotgun, teargas, cannon, grenade,
mace, slingshot, whip.
• Pleasant: As per previous experiment with insects and flowers.
• Unpleasant: As per previous experiment with insects and flowers.
WEAT_DATA 3: We use the European American and African American names along with pleasant
and unpleasant attributes found in (5). Names that are marked with italics are excluded from
our replication. In the case of African American names this was due to being to infrequent to
occur in GloVe’s Common Crawl corpus; in the case of European American names an equal
number were deleted, chosen at random.
• European American names: Adam, Chip, Harry, Josh, Roger, Alan, Frank, Ian, Justin,
Ryan, Andrew, Fred, Jack, Matthew, Stephen, Brad, Greg, Jed, Paul, Todd, Brandon,
Hank, Jonathan, Peter, Wilbur, Amanda, Courtney, Heather, Melanie, Sara, Amber, Crystal,
Katie, Meredith, Shannon, Betsy, Donna, Kristin, Nancy, Stephanie, Bobbie-Sue,
Ellen, Lauren, Peggy, Sue-Ellen, Colleen, Emily, Megan, Rachel, Wendy.
• African American names: Alonzo, Jamel, Lerone, Percell, Theo, Alphonse, Jerome,
Leroy, Rasaan, Torrance, Darnell, Lamar, Lionel, Rashaun, Tyree, Deion, Lamont, Malik,
Terrence, Tyrone, Everol, Lavon, Marcellus, Terryl, Wardell, Aiesha, Lashelle, Nichelle,
Shereen, Temeka, Ebony, Latisha, Shaniqua, Tameisha, Teretha, Jasmine, Latonya, Shanise,
Tanisha, Tia, Lakisha, Latoya, Sharise, Tashika, Yolanda, Lashandra, Malika, Shavonn,
Tawanda, Yvette.
• Pleasant: caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond,
gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family,
happy, laughter, paradise, vacation.
• Unpleasant: abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink,
assault, disaster, hatred, pollute, tragedy, bomb, divorce, jail, poverty, ugly, cancer, evil,
kill, rotten, vomit.
WEAT_DATA 4: We use the European American and African American names from (7), along with
pleasant and unpleasant attributes found in (5).
• European American names: Brad, Brendan, Geoffrey, Greg, Brett, Jay, Matthew, Neil,
Todd, Allison, Anne, Carrie, Emily, Jill, Laurie, Kristen, Meredith, Sarah.
• African American names: Darnell, Hakim, Jermaine, Kareem, Jamal, Leroy, Rasheed,
Tremayne, Tyrone, Aisha, Ebony, Keisha, Kenya, Latonya, Lakisha, Latoya, Tamika,
Tanisha.
• Pleasant: caress, freedom, health, love, peace, cheer, friend, heaven, loyal, pleasure, diamond,
gentle, honest, lucky, rainbow, diploma, gift, honor, miracle, sunrise, family,
happy, laughter, paradise, vacation.
• Unpleasant: abuse, crash, filth, murder, sickness, accident, death, grief, poison, stink,
assault, disaster, hatred, pollute, tragedy, bomb, divorce, jail, poverty, ugly, cancer, evil,
kill, rotten, vomit.
WEAT_DATA 5: We use the European American and African American names from (7), along with
pleasant and unpleasant attributes found in (9).
• European American names: Brad, Brendan, Geoffrey, Greg, Brett, Jay, Matthew, Neil,
Todd, Allison, Anne, Carrie, Emily, Jill, Laurie, Kristen, Meredith, Sarah.
• African American names: Darnell, Hakim, Jermaine, Kareem, Jamal, Leroy, Rasheed,
Tremayne, Tyrone, Aisha, Ebony, Keisha, Kenya, Latonya, Lakisha, Latoya, Tamika,
Tanisha.
• Pleasant: joy, love, peace, wonderful, pleasure, friend, laughter, happy.
• Unpleasant: agony, terrible, horrible, nasty, evil, war, awful, failure.
WEAT_DATA 6: We use the male and female names along with career and family attributes found
in (9).
• Male names: John, Paul, Mike, Kevin, Steve, Greg, Jeff, Bill.
• Female names: Amy, Joan, Lisa, Sarah, Diana, Kate, Ann, Donna.
• Career: executive, management, professional, corporation, salary, office, business, career.
• Family: home, parents, children, family, cousins, marriage, wedding, relatives.
WEAT_DATA 7: We use the math and arts target words along with male and female attributes found
in (9).
• Math: math, algebra, geometry, calculus, equations, computation, numbers, addition.
• Arts: poetry, art, dance, literature, novel, symphony, drama, sculpture.
• Male terms: male, man, boy, brother, he, him, his, son.
• Female terms: female, woman, girl, sister, she, her, hers, daughter.
WEAT_DATA 8: We use the science and arts target words along with male and female attributes
found in (10).
• Science: science, technology, physics, chemistry, Einstein, NASA, experiment, astronomy.
• Arts: poetry, art, Shakespeare, dance, literature, novel, symphony, drama.
• Male terms: brother, father, uncle, grandfather, son, he, his, him.
• Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her.
WEAT_DATA 9: We use the mental and physical disease target words along with uncontrollability
and controllability attributes found in (23).
• Mental disease: sad, hopeless, gloomy, tearful, miserable, depressed.
• Physical disease: sick, illness, influenza, disease, virus, cancer.
• Temporary: impermanent, unstable, variable, fleeting, short-term, brief, occasional.
• Permanent: stable, always, constant, persistent, chronic, prolonged, forever.
WEAT_DATA 10: We use young and old people’s names as target words along with pleasant and
unpleasant attributes found in (9).
• Young people’s names: Tiffany, Michelle, Cindy, Kristy, Brad, Eric, Joey, Billy.
• Old people’s names: Ethel, Bernice, Gertrude, Agnes, Cecil, Wilbert, Mortimer, Edgar.
• Pleasant: joy, love, peace, wonderful, pleasure, friend, laughter, happy.
• Unpleasant: agony, terrible, horrible, nasty, evil, war, awful, failure.'''


def parse_line(line):
    name, words_str = line[1:].split(': ')
    return {'name': name, 'words': words_str[:-2].split(', ')}


def parse_case(case):
    groups_str = case.replace('\n', ' ').split('•')[1:]
    return {'first_target': parse_line(groups_str[0]),
            'second_target': parse_line(groups_str[1]),
            'first_attribute': parse_line(groups_str[2]),
            'second_attribute': parse_line(groups_str[3])}


cases = TEXT.split('WEAT_DATA')[1:]

WEAT_DATA = [parse_case(case) for case in cases]

WEAT_DATA[1]['first_attribute']['words'] = WEAT_DATA[0]['first_attribute']['words']
WEAT_DATA[1]['second_attribute']['words'] = WEAT_DATA[0]['second_attribute']['words']

WEAT_DATA[2]['first_target']['remove'] = ['Chip', 'Ian', 'Fred', 'Jed', 'Todd', 'Brandon', 'Hank', 'Wilbur', 'Sara', 'Amber', 'Crystal', 'Meredith', 'Shannon', 'Donna', 'Bobbie-Sue', 'Peggy', 'Sue-Ellen', 'Wendy']
WEAT_DATA[2]['second_target']['remove'] = ['Lerone', 'Percell', 'Rasaan', 'Rashaun', 'Everol', 'Terryl', 'Aiesha', 'Lashelle', 'Temeka', 'Tameisha', 'Teretha', 'Latonya', 'Shanise', 'Sharise', 'Tashika', 'Lashandra', 'Shavonn', 'Tawanda']

print(len(WEAT_DATA[2]['first_target']['remove']), len(WEAT_DATA[2]['second_target']['remove']))
assert len(WEAT_DATA[2]['first_target']['remove']) == len(WEAT_DATA[2]['second_target']['remove'])
assert set(WEAT_DATA[2]['first_target']['remove']).issubset(WEAT_DATA[2]['first_target']['words'])

WEAT_DATA[3]['first_target']['remove'] = ['Jay', 'Kristen']
WEAT_DATA[3]['second_target']['remove'] = ['Tremayne', 'Latonya']

print(len(WEAT_DATA[3]['first_target']['remove']), len(WEAT_DATA[3]['second_target']['remove']))
assert len(WEAT_DATA[3]['first_target']['remove']) == len(WEAT_DATA[3]['second_target']['remove'])
assert set(WEAT_DATA[3]['first_target']['remove']).issubset(WEAT_DATA[3]['first_target']['words'])

WEAT_DATA[4]['first_target']['remove'] = ['Jay', 'Kristen']
WEAT_DATA[4]['second_target']['remove'] = ['Tremayne', 'Latonya']

print(len(WEAT_DATA[4]['first_target']['remove']), len(WEAT_DATA[4]['second_target']['remove']))
assert len(WEAT_DATA[4]['first_target']['remove']) == len(WEAT_DATA[4]['second_target']['remove'])
assert set(WEAT_DATA[4]['first_target']['remove']).issubset(WEAT_DATA[4]['first_target']['words'])


WEAT_DATA[0]['original_finding'] = {'Ref': 'A. G. Greenwald, D. E. McGhee, J. L. Schwartz, Measuring individual differences in im- plicit cognition: the implicit association test., Journal of personality and social psychology 74, 1464 (1998).',
                                    'N': '32',
                                    'd': '1.35',
                                    'p': '1e-8'}

WEAT_DATA[1]['original_finding'] = {'Ref': 'A. G. Greenwald, D. E. McGhee, J. L. Schwartz, Measuring individual differences in im- plicit cognition: the implicit association test., Journal of personality and social psychology 74, 1464 (1998).',
                                    'N': '32',
                                    'd': '1.66',
                                    'p': '1e-10'}

WEAT_DATA[2]['original_finding'] = {'Ref': 'A. G. Greenwald, D. E. McGhee, J. L. Schwartz, Measuring individual differences in im- plicit cognition: the implicit association test., Journal of personality and social psychology 74, 1464 (1998).',
                                    'N': '26',
                                    'd': '1.17',
                                    'p': '1e-5'}

WEAT_DATA[3]['original_finding'] = {'Ref': 'M. Bertrand, S. Mullainathan, Are Emily and Greg more employable than Lakisha and Jamal? a field experiment on labor market discrimination, The American Economic Review 94, 991 (2004).',
                                    'N': '',
                                    'd': '',
                                    'p': ''}

WEAT_DATA[4]['original_finding'] = {'Ref': 'M. Bertrand, S. Mullainathan, Are Emily and Greg more employable than Lakisha and Jamal? a field experiment on labor market discrimination, The American Economic Review 94, 991 (2004).',
                                    'N': '',
                                    'd': '',
                                    'p': ''}

WEAT_DATA[5]['original_finding'] = {'Ref': 'B. A. Nosek, M. Banaji, A. G. Greenwald, Harvesting implicit group attitudes and beliefs from a demonstration web site., Group Dynamics: Theory, Research, and Practice 6, 101 (2002).',
                                    'N': '39k',
                                    'd': '0.72',
                                    'p': '< 1e-2'}

WEAT_DATA[6]['original_finding'] = {'Ref': 'B. A. Nosek, M. Banaji, A. G. Greenwald, Harvesting implicit group attitudes and beliefs from a demonstration web site., Group Dynamics: Theory, Research, and Practice 6, 101 (2002).',
                                    'N': '28k',
                                    'd': '0.82',
                                    'p': '< 1e-2'}

WEAT_DATA[7]['original_finding'] = {'Ref': 'B. A. Nosek, M. R. Banaji, A. G. Greenwald, Math=male, me=female, therefore math̸=me., Journal of Personality and Social Psychology 83, 44 (2002).',
                                    'N': '91',
                                    'd': '1.47',
                                    'p': '1e-24'}

WEAT_DATA[8]['original_finding'] = {'Ref': 'P. D. Turney, P. Pantel, From frequency to meaning: Vector space models of semantics, Journal of Artificial Intelligence Research 37, 141 (2010).',
                                    'N': '135',
                                    'd': '1.01',
                                    'p': '1e-3'}


WEAT_DATA[9]['original_finding'] = {'Ref': 'B. A. Nosek, M. Banaji, A. G. Greenwald, Harvesting implicit group attitudes and beliefs from a demonstration web site., Group Dynamics: Theory, Research, and Practice 6, 101 (2002).',
                                    'N': '43k',
                                    'd': '1.42',
                                    'p': '< 1e-2'}

json.dump(WEAT_DATA, open('weat.json', 'w'), indent=True)