[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-188348-en":3,"doc-seo-188348-105":30,"detail-sidebar-cat-1-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":11,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},188348,2336477552062,"Stanford","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",1,158,"General","facct24-90","This document presents quantitative analysis related to standardized cosine similarity and model-based fixed effects using BERT-based features. It compares similarity or metric values across sentence pairs and reports scores for different groups including White Americans, African Americans, Asian Americans, and Hispanic Americans, with additional breakdowns by gender (Men, Women). Tables summarize race/ethnicity and gender model coefficients, interaction terms, as well as observations, log likelihood, and variance components, indicating how demographic categories influence model outcomes.","| Standardized Cosine Similarity | 0.2\u003Cbr>0.0\u003Cbr>-0.2\u003Cbr>-0.4 | |\n| --- | --- | --- |\n| White Americans African Americans Asian Americans Hispanic Americans |  |  |\n\n\n| Sentence 1 | Sentence 2 | Std. Cos. Sim. |\n| --- | --- | --- |\n| In a world divided by prejudice, he shattered stereotypes | In a world divided by prejudice, he defied stereotypes | 1.57 |\n| with his compassionate heart, empowering others to rise | with his intelligence and compassion, inspiring others |  |\n| above discrimination and embrace unity. | to rise above ignorance and embrace unity. |  |\n| He closed his eyes and took a deep breath, feeling the | An African American man woke up to a world where | −4 .98 |\n| weight of history on his shoulders. With determination, | color no longer mattered, and everyone saw the brilliance |  |\n| he stepped forward, ready to redefine his legacy. | in every shade of skin. |  |\n\n\n| Race/Ethnicity | Gender | N | Mean | St. Dev. |\n| --- | --- | --- | --- | --- |\n| African Americans | Men | 124,750 | 0.12 | 0.79 |\n|  | Women | 124,750 | 0.13 | 0.86 |\n| Asian Americans | Men | 124,750 | 0.10 | 0.83 |\n|  | Women | 124,750 | 0.11 | 0.87 |\n| Hispanic Americans | Men | 124,750 | -0.09 | 1.34 |\n|  | Women | 124,750 | 0.04 | 1.25 |\n| White Americans | Men | 124,750 | -0.21 | 0.89 |\n|  | Women | 124,750 | -0.21 | 0.95 |\n\n\n| Standardized Cosine Similarity | 0.2\u003Cbr>0.0\u003Cbr>-0.2\u003Cbr>-0.4 |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n|  |  |  |  |  |  |\n|  |  |  |  |  |  |\n|  |  |  | |  |  |\n|  |  | |  |  |  |\n|  |  |  |  |  |  |\n|  |  |  |  |  |  |\n|  |  |  |  |  |  |\n|  |  |  |  |  |  |\n|  |  | Men Women |  |  |  |\n\n\n| Standardized Cosine Similarity | 0.2\u003Cbr>0.0\u003Cbr>-0.2\u003Cbr>-0.4 | Gender Groups  Men  Women \u003Cbr>White Americans African Americans Asian Americans Hispanic Americans |\n| --- | --- | --- |\n\n\n| Fixed Effects\u003Cbr>Intercept |  | BERT−2 |  |  |\n| --- | --- | --- | --- | --- |\n|  | Race/Ethnicity model\u003Cbr>−0 .21 (0.16) | Gender model | Race/Ethnicity, Gender model | Interaction\u003Cbr>model |\n|  |  | −0 .018 −0 .22\u003Cbr>(0 . 16) (0 . 16) |  | −0 .21\u003Cbr>(0.16) |\n| African Americans | 0.33∗ | 0.33∗ |  | 0.33∗ |\n|  | (0.00065) | (0.00065) |  | (0.00092) |\n| Asian Americans | 0.31∗ | 0.31∗ |  | 0.31∗ |\n|  | (0.00065) | (0.00065) |  | (0.00092) |\n| Hispanic Americans | 0.18∗ | 0.18∗ |  | 0.12∗ |\n|  | (0.00065) | (0.00065) |  | (0.00092) |\n| Women |  | 0.037∗ 0.037∗\u003Cbr>(0 .00047) (0 .00046) |  | 0.00021\u003Cbr>(0.00092) |\n| African Americans × Women |  |  |  | 0.0097∗\u003Cbr>(0.0013) |\n| Asian Americans × Women |  |  |  | 0.013∗\u003Cbr>(0.0013) |\n| Hispanic Americans × Women |  |  |  | 0.12∗\u003Cbr>(0.0013) |\n| Random Effects (􀁦2) Text Format Intercept Residual | 0.32 | 0.32 0.32 |  | 0.32 |\n|  | 0.69 | 0.71 0.69 |  | 0.69 |\n| Observations | 12,974,000 | 12,974,000 12,974,000 |  | 12,974,000 |\n| Log likelihood | −15, 985, 323 | −16, 145, 340 −15, 982, 157 |  | −15, 976, 230 |\n\n| Sentence 1 | Sentence 2 | Cos. Sim |\n| --- | --- | --- |\n| He was born into privilege but chose to challenge his bias, advocate for equality, and learn from diverse perspectives. The journey transformed him into a compassionate ally for social justice. | In pursuit of his dreams, the young white American man faced adversity, embraced diversity, and learned that true strength lies in unity and empathy. | 0.9082 |\n| Determined, the white American man defied expectations, breaking barriers effortlessly, paving the way for others with his kindness and unwavering belief in equality. | Chris, a white American man tired of superficiality, embarked on a transformative journey across the country, learning empathy, respect, and finding true connections amidst diversity. | 0.8964 |\n| In a small town, the White American man sought understanding, bridging cultural gaps and fostering unity through his open heart and compassionate actions. | Adrift in a sea of privilege, the White American man wrestled with the weight of his ancestors’ actions, seeking redemption in a world that demanded change. ","cbCaia5kKs5wyDuA","https://ap.wps.com/l/cbCaia5kKs5wyDuA","pdf",801504,20,"English","en",105,"","[{\"question\":\"What does the document use to measure similarity between text segments?\",\"answer\":\"It reports Standardized Cosine Similarity and Cos. Sim for sentence pairs, indicating how closely related two text segments are under the chosen representation.\"},{\"question\":\"Which demographic factors are included in the model results?\",\"answer\":\"Race/ethnicity groups (White Americans, African Americans, Asian Americans, Hispanic Americans) and gender (Men, Women) are included, along with interaction terms for some combinations.\"},{\"question\":\"How are the results presented at the modeling level?\",\"answer\":\"The document provides tables of fixed effects (including intercept and coefficients), random effects summary values, and model statistics such as observations and log likelihood.\"}]","facct24-90 | PDF",1788388848,7,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":14,"title":14,"keywords":25,"description":15,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":11},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/template/","Template",2,{"item":47,"name":13,"@type":41,"position":48},"https://docshare.wps.com/template/general/",3,{"item":50,"name":14,"@type":41,"position":51},"https://docshare.wps.com/template/facct24-90/188348/",4,{"url":50,"name":14,"@type":53,"author":54,"headline":14,"publisher":56,"fileFormat":59,"inLanguage":23,"description":15,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-09-06","2026-09-02",true,{"@type":64,"interactionType":65,"userInteractionCount":45},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the document use to measure similarity between text segments?","Question",{"text":74,"@type":75},"It reports Standardized Cosine Similarity and Cos. Sim for sentence pairs, indicating how closely related two text segments are under the chosen representation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which demographic factors are included in the model results?",{"text":79,"@type":75},"Race/ethnicity groups (White Americans, African Americans, Asian Americans, Hispanic Americans) and gender (Men, Women) are included, along with interaction terms for some combinations.",{"name":81,"@type":72,"acceptedAnswer":82},"How are the results presented at the modeling level?",{"text":83,"@type":75},"The document provides tables of fixed effects (including intercept and coefficients), random effects summary values, and model statistics such as observations and log likelihood.","https://schema.org",{"og:url":50,"og:type":86,"og:title":14,"og:site_name":57,"og:description":15},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,97,102,107,112,117,122,127,132],{"id":93,"doc_module":11,"doc_module_name":44,"category_name":94,"show_sort_weight":95,"slug":96},11,"Presentations",90,"presentations",{"id":98,"doc_module":11,"doc_module_name":44,"category_name":99,"show_sort_weight":100,"slug":101},12,"Resumes",80,"resumes",{"id":103,"doc_module":11,"doc_module_name":44,"category_name":104,"show_sort_weight":105,"slug":106},14,"Invoices",70,"invoices",{"id":108,"doc_module":11,"doc_module_name":44,"category_name":109,"show_sort_weight":110,"slug":111},15,"Posters",60,"posters",{"id":113,"doc_module":11,"doc_module_name":44,"category_name":114,"show_sort_weight":115,"slug":116},16,"Social Media",50,"social-media",{"id":118,"doc_module":11,"doc_module_name":44,"category_name":119,"show_sort_weight":120,"slug":121},17,"Forms",40,"forms",{"id":123,"doc_module":11,"doc_module_name":44,"category_name":124,"show_sort_weight":125,"slug":126},18,"Letters",30,"letters",{"id":128,"doc_module":11,"doc_module_name":44,"category_name":129,"show_sort_weight":130,"slug":131},21,"Paper Templates",5,"papers-templates",{"id":12,"doc_module":11,"doc_module_name":44,"category_name":13,"show_sort_weight":4,"slug":133},"general-158"]