{"id":"https://openalex.org/W7035912165","doi":null,"title":"An Analysis of Gender Bias in K-12 Assigned Literature Through Comparison of Non-Contextual Word Embedding Models","display_name":"An Analysis of Gender Bias in K-12 Assigned Literature Through Comparison of Non-Contextual Word Embedding Models","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W7035912165"},"language":"en","primary_location":{"id":"pmh:oai:digital.lib.washington.edu:1773/46827","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1773/46827","pdf_url":"http://hdl.handle.net/1773/46827","source":{"id":"https://openalex.org/S4306401128","display_name":"ResearchWorks at the University of Washington (University of Washington)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I201448701","host_organization_name":"University of Washington","host_organization_lineage":["https://openalex.org/I201448701"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/1773/46827","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mohan, Preeti","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohan, Preeti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T13173","display_name":"Gender Studies in Language","score":0.8970000147819519,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13173","display_name":"Gender Studies in Language","score":0.8970000147819519,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.019500000402331352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12908","display_name":"Media, Gender, and Advertising","score":0.01269999984651804,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gender-bias","display_name":"gender bias","score":0.9620000123977661},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.6729999780654907},{"id":"https://openalex.org/keywords/children-s-literature","display_name":"children's literature","score":0.6679999828338623},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2Vec","score":0.7379999756813049},{"id":"https://openalex.org/keywords/fasttext","display_name":"FastText","score":0.9879999756813049},{"id":"https://openalex.org/keywords/gloves","display_name":"gloves","score":1.0},{"id":"https://openalex.org/keywords/gendered-language","display_name":"gendered language","score":0.6439999938011169},{"id":"https://openalex.org/keywords/corpus-size","display_name":"corpus size","score":0.5320000052452087}],"concepts":[{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5126000046730042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5016999840736389},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4869000017642975},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3930000066757202},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38100001215934753},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2930000126361847},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27140000462532043},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.26840001344680786},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:digital.lib.washington.edu:1773/46827","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1773/46827","pdf_url":"http://hdl.handle.net/1773/46827","source":{"id":"https://openalex.org/S4306401128","display_name":"ResearchWorks at the University of Washington (University of Washington)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I201448701","host_organization_name":"University of Washington","host_organization_lineage":["https://openalex.org/I201448701"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:digital.lib.washington.edu:1773/46827","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/1773/46827","pdf_url":"http://hdl.handle.net/1773/46827","source":{"id":"https://openalex.org/S4306401128","display_name":"ResearchWorks at the University of Washington (University of Washington)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I201448701","host_organization_name":"University of Washington","host_organization_lineage":["https://openalex.org/I201448701"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[{"display_name":"Quality education","score":0.8278999924659729,"id":"https://metadata.un.org/sdg/4"},{"display_name":"Reduced inequalities","score":0.4000000059604645,"id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7035912165.pdf","grobid_xml":"https://content.openalex.org/works/W7035912165.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1549981646","https://openalex.org/W1614298861","https://openalex.org/W2064675550","https://openalex.org/W2079823081","https://openalex.org/W2117130368","https://openalex.org/W2140534852","https://openalex.org/W2169397596","https://openalex.org/W2250539671","https://openalex.org/W2251803266","https://openalex.org/W2493916176","https://openalex.org/W2616914001","https://openalex.org/W2740983644","https://openalex.org/W2754658590","https://openalex.org/W2769358515","https://openalex.org/W2787560479","https://openalex.org/W2803176955","https://openalex.org/W2893425640","https://openalex.org/W2896457183","https://openalex.org/W2920052239","https://openalex.org/W2926555354","https://openalex.org/W2948827028","https://openalex.org/W2949534740","https://openalex.org/W2952929029","https://openalex.org/W2963381846","https://openalex.org/W2972568911","https://openalex.org/W2972668795","https://openalex.org/W2974273066","https://openalex.org/W3016719704","https://openalex.org/W3030163527","https://openalex.org/W4292081290","https://openalex.org/W4386301836"],"related_works":["https://openalex.org/W7019053070","https://openalex.org/W3201242639","https://openalex.org/W2926555354","https://openalex.org/W2563153773","https://openalex.org/W6990497985","https://openalex.org/W2972586109","https://openalex.org/W4402615953","https://openalex.org/W3093722736","https://openalex.org/W3102581477","https://openalex.org/W3016719704"],"abstract_inverted_index":{"Word":[0],"embeddings":[1,161,186,212],"are":[2,24,121,141,187,199,227],"mathematical":[3],"representations":[4],"of":[5,11,64,75,99,158,223],"words":[6,22,44,51,225,244],"computed":[7],"from":[8,170,213],"a":[9,14,61,97,214,232,251],"group":[10,63],"texts":[12],"that":[13,23,119,184,226,234,245,253],"machine":[15],"learning":[16],"model":[17],"is":[18],"trained":[19],"on.":[20],"Generally,":[21],"similar":[25],"to":[26,48,53,102,111,124,146,163,174,190,202],"each":[27],"othersemantically":[28],"will":[29],"be":[30,46,54],"closer":[31],"together":[32],"in":[33,56,60,104,107,127,143,148,151,165,192,204,220],"the":[34,38,57,108,152,168,210,221,224,228],"vector-space":[35],"created":[36],"by":[37],"embedding":[39,78,180],"model.":[40],"The":[41],"distance":[42],"between":[43],"can":[45],"analyzed":[47],"understand":[49],"what":[50],"tend":[52],"used":[55],"same":[58],"contexts":[59],"given":[62],"texts.":[65],"In":[66],"this":[67],"thesis,":[68],"I":[69,182,217],"use":[70],"three":[71,113],"different":[72,156,166],"non-contextual":[73,159,178],"methods":[74],"training":[76],"word":[77,160,179,211],"models,":[79],"Word2Vec":[80,196],"(Mikolov":[81],"et":[82,87,93],"al.,":[83,88,94],"2013),":[84],"FastText":[85,198],"(Bojanowski":[86],"2017),":[89],"and":[90,131,197],"GloVe":[91,185],"(Pennington":[92],"2014),":[95],"on":[96],"corpus":[98],"literature":[100,144],"assigned":[101,145],"students":[103],"grades":[105,149],"K-12":[106,150],"United":[109,153],"States":[110],"answer":[112],"questions:-":[114],"It":[115],"has":[116],"been":[117],"shown":[118],"children":[120,147],"particularly":[122],"prone":[123],"internalize":[125],"biases":[126,140,191,203,238,256],"thecontent":[128],"they":[129],"read":[130],"watch":[132],"(Railsback,":[133],"1993;":[134],"Jacobs,":[135],"2003;":[136],"Slater,":[137],"2003).":[138],"What":[139],"present":[142],"States?\\n-":[154],"Are":[155],"kinds":[157],"sensitive":[162,189,201],"bias":[164,176],"ways?\\n-Is":[167],"text":[169],"one":[171],"book":[172,233,252],"enough":[173],"detect":[175],"using":[177],"models?":[181],"find":[183],"more":[188,200,247],"smaller":[193],"corpora,":[194],"while":[195],"large":[205],"corpora.":[206],"When":[207],"looking":[208],"at":[209],"single":[215],"book,":[216],"see":[218],"variations":[219],"strength":[222],"\u201cmost":[229],"gendered\u201d":[230],"\u2014":[231],"had":[235,243],"stronger":[236],"gender":[237,255],"(determined":[239],"through":[240,259],"literary":[241,260],"critiques)":[242],"were":[246],"strongly":[248],"gendered":[249],"than":[250],"subverted":[254],"(also":[257],"determined":[258],"critique).":[261]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2025-10-10T00:00:00"}
