An analysis of unconscious gender bias in academic texts by means of a decision algorithm
Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a...
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| Published in: | PloS one Vol. 16; no. 9; p. e0257903 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Public Library of Science
30.09.2021
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms. |
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| AbstractList | Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms. Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms.Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms. |
| Audience | Academic |
| Author | Orgeira-Crespo, Pedro Míguez-Álvarez, Carla Cuevas-Alonso, Miguel Rivo-López, Elena |
| AuthorAffiliation | 1 Department of Mechanical Engineering, Heat Engines and Machines, and Fluids, Aerospace Area, Aerospace Engineering School, University of Vigo, Vigo, Spain 3 Faculty of Economic Sciences, Business Organization and Marketing Department, University of Vigo, Vigo, Spain Newcastle University, UNITED KINGDOM 2 Language Variation and Textual Categorization (LVTC), Philology and Translation School, University of Vigo, Vigo, Spain |
| AuthorAffiliation_xml | – name: 1 Department of Mechanical Engineering, Heat Engines and Machines, and Fluids, Aerospace Area, Aerospace Engineering School, University of Vigo, Vigo, Spain – name: Newcastle University, UNITED KINGDOM – name: 2 Language Variation and Textual Categorization (LVTC), Philology and Translation School, University of Vigo, Vigo, Spain – name: 3 Faculty of Economic Sciences, Business Organization and Marketing Department, University of Vigo, Vigo, Spain |
| Author_xml | – sequence: 1 givenname: Pedro orcidid: 0000-0003-2734-4586 surname: Orgeira-Crespo fullname: Orgeira-Crespo, Pedro – sequence: 2 givenname: Carla surname: Míguez-Álvarez fullname: Míguez-Álvarez, Carla – sequence: 3 givenname: Miguel surname: Cuevas-Alonso fullname: Cuevas-Alonso, Miguel – sequence: 4 givenname: Elena surname: Rivo-López fullname: Rivo-López, Elena |
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| CitedBy_id | crossref_primary_10_1186_s41239_023_00380_y crossref_primary_10_1109_TSE_2024_3437355 crossref_primary_10_1016_j_jrurstud_2023_103166 |
| Cites_doi | 10.1109/GRC.2007.4403192 10.22266/ijies2016.0930.09 10.1371/journal.pone.0036156 10.1007/s10489-018-1242-y 10.1145/3308558.3313504 10.1007/s10115-014-0746-y 10.1093/bib/bbv087 10.1080/20445911.2013.861467 10.1016/j.knosys.2016.10.003 10.1177/0146167211406434 10.1016/j.eswa.2013.01.019 10.1109/MIS.2016.71 10.1016/j.proeng.2011.08.404 10.1038/nmeth.3945 10.1024/1421-0185/a000078 10.3390/math9060690 10.1145/3350546.3352512 10.1109/iMac4s.2013.6526500 10.1016/j.chb.2013.05.024 10.1016/j.engappai.2016.02.002 10.1075/impact.11 10.18653/v1/N19-1061 10.18653/v1/W19-3805 10.1126/science.337.6102.1592 10.18653/v1/D18-1521 10.1006/jmla.1997.2528 10.1007/s10993-012-9241-z 10.1075/impact.9.11pau 10.1177/0261927X12446599 10.1108/IJOES-05-2018-0079 10.1016/j.proeng.2014.03.129 10.21892/01239813.472 10.1016/j.fss.2007.04.014 10.1109/ICODSE.2015.7436992 10.1145/3278721.3278729 10.1007/s10936-010-9164-9 10.1016/j.eswa.2014.06.009 10.18653/v1/D19-1531 10.1088/1757-899X/263/4/042062 10.1002/ejsp.1924 10.1016/j.techfore.2017.12.018 10.1145/3195570.3195580 10.3390/math9070751 10.3115/v1/D14-1181 10.1145/2844110 10.1016/j.ijresmar.2018.09.009 10.1007/s10115-016-0924-1 10.1007/s10936-014-9314-6 10.18653/v1/N19-3002 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Orgeira-Crespo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Orgeira-Crespo et al 2021 Orgeira-Crespo et al |
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| DOI | 10.1371/journal.pone.0257903 |
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| References | pone.0257903.ref042 A. Association (pone.0257903.ref012) 2020 D. Tang (pone.0257903.ref025) 2014; 1 pone.0257903.ref085 A. Pauwels (pone.0257903.ref014) 2001 L. Litosseliti (pone.0257903.ref016) 2006 B. Trstenjak (pone.0257903.ref030) 2014; 69 G.K. Pitsilis (pone.0257903.ref071) 2018; 48 pone.0257903.ref088 Y. Lin (pone.0257903.ref029) 2014 L. Jiang (pone.0257903.ref031) 2016 pone.0257903.ref003 J. Prewitt-Freilino (pone.0257903.ref045) 2011 F. Del Vigna (pone.0257903.ref077) 2017 pone.0257903.ref082 E. Teso (pone.0257903.ref018) 2010 pone.0257903.ref081 pone.0257903.ref084 C.M Hill (pone.0257903.ref001) 2016 pone.0257903.ref083 J. Emerson (pone.0257903.ref009) 2017 T. Chakraborty (pone.0257903.ref066) 2016 M. Allahyari (pone.0257903.ref037) 2017 pone.0257903.ref006 J.-Y. Chen (pone.0257903.ref049) 2011; 40 pone.0257903.ref005 A. Pesce (pone.0257903.ref090) 2019; 6 K. Lakshmanna (pone.0257903.ref040) 2016; 9 C. Cacciari (pone.0257903.ref053) 1997; 37 Z. Waseem (pone.0257903.ref074) 2016 G.H. Gonzalez (pone.0257903.ref021) 2016; 17 R. Panigrahi (pone.0257903.ref044) 2021; 9 EIGE, E.I.f.G.E (pone.0257903.ref011) 2018 M. Formanowicz (pone.0257903.ref052) 2013; 43 B.S. Kumar (pone.0257903.ref022) 2016; 114 pone.0257903.ref058 O. Sarrasin (pone.0257903.ref047) 2012; 71 E. Merkel (pone.0257903.ref054) 2012; 31 Á. García Meseguer (pone.0257903.ref059) 1994 V. Basile (pone.0257903.ref095) 2019 N. Diakopoulos (pone.0257903.ref086) 2016; 59 S. Yong-feng (pone.0257903.ref062) 2004 M. Calero Fernández (pone.0257903.ref056) 1999 S. Wang (pone.0257903.ref032) 2015; 44 Y. Dong (pone.0257903.ref050) 2015; 44 J. Hartmann (pone.0257903.ref060) 2019; 36 W. Zhang (pone.0257903.ref034) 2011; 15 S. Sharifirad (pone.0257903.ref070) 2018 A.B. Diehl (pone.0257903.ref008) 2016; 27 pone.0257903.ref024 pone.0257903.ref068 pone.0257903.ref067 S. Romaine (pone.0257903.ref015) 2001 L. Qiu (pone.0257903.ref051) 2012; 7 pone.0257903.ref069 E. Teso (pone.0257903.ref013) 2013; 12 H. Gonen (pone.0257903.ref065) 2019 M. Hellinger (pone.0257903.ref017) 2003 R. Jindal (pone.0257903.ref038) 2015 M. Gustafsson Sendén (pone.0257903.ref048) 2015 T. R. Gadekallu (pone.0257903.ref035) 2021 Y. Hitti (pone.0257903.ref080) 2019 M. Gustafsson Sendén (pone.0257903.ref091) 2015; 6 A. Opoku (pone.0257903.ref010) 2019; 35 pone.0257903.ref028 E. Teso (pone.0257903.ref078) 2018; 129 T. Davidson (pone.0257903.ref076) 2017 J. Mervis (pone.0257903.ref004) 2012; 337 A. Lévy (pone.0257903.ref055) 2014; 26 pone.0257903.ref075 pone.0257903.ref033 J.L. Castro (pone.0257903.ref026) 2007; 158 pone.0257903.ref079 A. Ortigosa (pone.0257903.ref087) 2014; 31 J.B. Parks (pone.0257903.ref094) 2008; 27 M.M. Mostafa (pone.0257903.ref019) 2013; 40 S. Sczesny (pone.0257903.ref092) 2015; 41 A. Khadjeh Nassirtoussi (pone.0257903.ref020) 2014; 41 J. Stout (pone.0257903.ref046) 2011; 37 M. Haddoud (pone.0257903.ref027) 2016; 49 B. Agarwal (pone.0257903.ref036) 2014 M. Calero Fernández (pone.0257903.ref057) 2015 pone.0257903.ref073 S.R. Madsen (pone.0257903.ref002) 2018; 12 K. Lakshmanna (pone.0257903.ref041) 2016; 8 R. Panigrahi (pone.0257903.ref043); 9 P. Orgeira (pone.0257903.ref061) pone.0257903.ref039 L.L Bierema (pone.0257903.ref007) 2017 K.M. Douglas (pone.0257903.ref093) 2014; 33 R. Mihalcea (pone.0257903.ref072) 2016; 31 V. Jagtap (pone.0257903.ref023) 2013; 2 A. Danesh (pone.0257903.ref063) 2007 J. Lever (pone.0257903.ref089) 2016; 13 A. Basu (pone.0257903.ref064) 2003 |
| References_xml | – ident: pone.0257903.ref033 doi: 10.1109/GRC.2007.4403192 – volume: 9 start-page: 91 year: 2016 ident: pone.0257903.ref040 article-title: Constraint-based measures for DNA sequence mining using group search optimization algorithm publication-title: International Journal of Intelligent Engineering & systems doi: 10.22266/ijies2016.0930.09 – year: 2004 ident: pone.0257903.ref062 article-title: Comparison of text categorization algorithms publication-title: Wuhan University Journal of Natural Sciences – volume-title: A comparative study of gender-based linguistic reform across four European countries year: 2010 ident: pone.0257903.ref018 – volume: 7 start-page: e36156 issue: 5 year: 2012 ident: pone.0257903.ref051 article-title: The role of gender information in pronoun resolution: evidence from Chinese publication-title: PLoS One doi: 10.1371/journal.pone.0036156 – volume: 48 start-page: 4730 issue: 12 year: 2018 ident: pone.0257903.ref071 article-title: , Effective hate-speech detection in Twitter data using recurrent neural networks publication-title: Applied Intelligence doi: 10.1007/s10489-018-1242-y – ident: pone.0257903.ref075 doi: 10.1145/3308558.3313504 – volume: 44 start-page: 77 issue: 1 year: 2015 ident: pone.0257903.ref032 article-title: Adapting naive Bayes tree for text classification publication-title: Knowledge and Information Systems doi: 10.1007/s10115-014-0746-y – volume-title: SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter year: 2019 ident: pone.0257903.ref095 – volume: 12 start-page: 62 issue: 1 year: 2018 ident: pone.0257903.ref002 publication-title: Unconscious Gender Bias: Implications for Women’s Leadership Development – volume: 17 start-page: 33 issue: 1 year: 2016 ident: pone.0257903.ref021 article-title: Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery publication-title: Brief Bioinform doi: 10.1093/bib/bbv087 – volume: 26 start-page: 27 issue: 1 year: 2014 ident: pone.0257903.ref055 article-title: Fostering the generic interpretation of grammatically masculine forms: When my aunt could be one of the mechanics publication-title: Journal of Cognitive Psychology doi: 10.1080/20445911.2013.861467 – volume: 41 start-page: 943 issue: 7 year: 2015 ident: pone.0257903.ref092 publication-title: Beyond Sexist Beliefs:How Do People Decide to Use Gender-Inclusive Language? – volume: 114 start-page: 128 year: 2016 ident: pone.0257903.ref022 article-title: A survey of the applications of text mining in financial domain publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2016.10.003 – volume: 37 start-page: 757 year: 2011 ident: pone.0257903.ref046 article-title: When He Doesn’t Mean You: Gender-Exclusive Language as Ostracism publication-title: Personality & social psychology bulletin doi: 10.1177/0146167211406434 – start-page: 66 volume-title: Kevin; Benson, Kathleen; Handley, Grace, Barriers and bias: The status of women in leadership year: 2016 ident: pone.0257903.ref001 – volume: 40 start-page: 4241 issue: 10 year: 2013 ident: pone.0257903.ref019 article-title: More than words: Social networks’ text mining for consumer brand sentiments publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.01.019 – start-page: 1 volume-title: Hand gesture classification using a novel CNN-crow search algorithm year: 2021 ident: pone.0257903.ref035 – start-page: 103 year: 2003 ident: pone.0257903.ref064 publication-title: Support Vector Machines for Text Categorization – volume: 31 start-page: 62 issue: 4 year: 2016 ident: pone.0257903.ref072 article-title: What Men Say, What Women Hear: Finding Gender-Specific Meaning Shades publication-title: IEEE Intelligent Systems doi: 10.1109/MIS.2016.71 – volume: 15 start-page: 2160 year: 2011 ident: pone.0257903.ref034 article-title: An Improvement to Naive Bayes for Text Classification publication-title: Procedia Engineering doi: 10.1016/j.proeng.2011.08.404 – ident: pone.0257903.ref005 – volume: 27 start-page: 181 issue: 2 year: 2016 ident: pone.0257903.ref008 publication-title: Making the Invisible Visible: A Cross-Sector Analysis of Gender-Based Leadership Barriers – volume: 13 start-page: 603 issue: 8 year: 2016 ident: pone.0257903.ref089 article-title: Classification evaluation publication-title: Nature Methods doi: 10.1038/nmeth.3945 – volume-title: Toolkit on Gender-sensitive Communication year: 2018 ident: pone.0257903.ref011 – volume: 71 start-page: 113 year: 2012 ident: pone.0257903.ref047 article-title: Sexism and Attitudes Toward Gender-Neutral Language The Case of English, French, and German publication-title: Swiss Journal of Psychology doi: 10.1024/1421-0185/a000078 – ident: pone.0257903.ref081 – volume: 9 start-page: 690 issue: 6 ident: pone.0257903.ref043 article-title: Performance Assessment of supervised classifiers for designing intrusion detection systems: A comprehensive review and recommendations for future research publication-title: Mathematics doi: 10.3390/math9060690 – start-page: 701 year: 2014 ident: pone.0257903.ref036 publication-title: Text Classification Using Machine Learning Methods-A Survey – ident: pone.0257903.ref069 doi: 10.1145/3350546.3352512 – ident: pone.0257903.ref024 doi: 10.1109/iMac4s.2013.6526500 – year: 2017 ident: pone.0257903.ref077 publication-title: Hate me, hate me not: Hate speech detection on Facebook – volume: 31 start-page: 527 year: 2014 ident: pone.0257903.ref087 article-title: Sentiment analysis in Facebook and its application to e-learning publication-title: Computers in Human Behavior doi: 10.1016/j.chb.2013.05.024 – start-page: 26 year: 2016 ident: pone.0257903.ref031 article-title: Deep feature weighting for naive Bayes and its application to text classification publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2016.02.002 – year: 2015 ident: pone.0257903.ref038 article-title: Techniques for text classification: Literature review and current trends publication-title: Webology – volume: 27 start-page: 276 issue: 3 year: 2008 ident: pone.0257903.ref094 publication-title: Generation Gaps in Attitudes Toward Sexist/Nonsexist Language – volume-title: Gender Across year: 2003 ident: pone.0257903.ref017 doi: 10.1075/impact.11 – volume-title: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them year: 2019 ident: pone.0257903.ref065 doi: 10.18653/v1/N19-1061 – ident: pone.0257903.ref079 doi: 10.18653/v1/W19-3805 – volume: 337 start-page: 1592 issue: 6102 year: 2012 ident: pone.0257903.ref004 article-title: U.S. Study Shows Unconscious Gender Bias in Academic Science publication-title: Science doi: 10.1126/science.337.6102.1592 – volume-title: English. A corpus-based view of gender in British and American English, in Gender Across Languages year: 2001 ident: pone.0257903.ref015 – start-page: 107 year: 2018 ident: pone.0257903.ref070 publication-title: Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs – ident: pone.0257903.ref083 doi: 10.18653/v1/D18-1521 – volume: 2 start-page: 164 year: 2013 ident: pone.0257903.ref023 article-title: Analysis of different approaches to Sentence-Level Sentiment Classification publication-title: International Journal of Scientific Engineering and Technology – volume: 37 start-page: 517 issue: 4 year: 1997 ident: pone.0257903.ref053 article-title: When Words Have Two Genders: Anaphor Resolution for Italian Functionally Ambiguous Words publication-title: Journal of Memory and Language doi: 10.1006/jmla.1997.2528 – volume-title: Una investigación sobre el género gramatical year: 1994 ident: pone.0257903.ref059 – volume: 12 start-page: 139 issue: 2 year: 2013 ident: pone.0257903.ref013 article-title: Gender-based linguistic reform in international organisations publication-title: Language Policy doi: 10.1007/s10993-012-9241-z – start-page: 41 volume-title: Decision Algorithm for the Automatic Determination of the Use of Non-Inclusive Terms in Academic Texts ident: pone.0257903.ref061 – ident: pone.0257903.ref006 – volume: 6 issue: 893 year: 2015 ident: pone.0257903.ref091 publication-title: Introducing a gender-neutral pronoun in a natural gender language: the influence of time on attitudes and behavior – start-page: 88 year: 2016 ident: pone.0257903.ref074 article-title: Hateful Symbols or Hateful People? publication-title: Predictive Features for Hate Speech Detection on Twitter – ident: pone.0257903.ref058 – volume-title: English. Spreading the feminist word: The case of the new courtesy title Ms in Australian English year: 2001 ident: pone.0257903.ref014 doi: 10.1075/impact.9.11pau – volume: 1 start-page: 1555 year: 2014 ident: pone.0257903.ref025 publication-title: Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification – volume: 33 start-page: 667 issue: 6 year: 2014 ident: pone.0257903.ref093 article-title: “A Giant Leap for Mankind” But What About Women? publication-title: The Role of System-Justifying Ideologies in Predicting Attitudes Toward Sexist Language – volume: 31 start-page: 311 issue: 3 year: 2012 ident: pone.0257903.ref054 article-title: Shielding Women Against Status Loss:The Masculine Form and Its Alternatives in the Italian Language publication-title: Journal of Language and Social Psychology doi: 10.1177/0261927X12446599 – start-page: 225 volume-title: Sexismo lingüístico. Análisis y propuestas ante la discriminación sexual en el lenguaje year: 1999 ident: pone.0257903.ref056 – volume: 35 start-page: 2 issue: 1 year: 2019 ident: pone.0257903.ref010 article-title: Second-generation gender bias publication-title: International Journal of Ethics and Systems doi: 10.1108/IJOES-05-2018-0079 – volume: 69 start-page: 1356 year: 2014 ident: pone.0257903.ref030 article-title: KNN with TF-IDF based Framework for Text Categorization publication-title: Procedia Engineering doi: 10.1016/j.proeng.2014.03.129 – start-page: 1 year: 2007 ident: pone.0257903.ref063 publication-title: Improve text classification accuracy based on classifier fusion methods – ident: pone.0257903.ref039 – volume: 6 start-page: 472 issue: 23 year: 2019 ident: pone.0257903.ref090 article-title: Actitudes y Uso del Lenguaje Inclusivo según el Género y la Edad publication-title: Búsqueda doi: 10.21892/01239813.472 – volume: 158 start-page: 2057 issue: 18 year: 2007 ident: pone.0257903.ref026 article-title: Extraction of fuzzy rules from support vector machines publication-title: Fuzzy Sets and Systems doi: 10.1016/j.fss.2007.04.014 – ident: pone.0257903.ref028 doi: 10.1109/ICODSE.2015.7436992 – ident: pone.0257903.ref068 doi: 10.1145/3278721.3278729 – volume-title: A Filtering Methodology for the Gender Generalization Subtype year: 2019 ident: pone.0257903.ref080 – year: 2006 ident: pone.0257903.ref016 publication-title: Gender and language: Theory and practice – start-page: 145 volume-title: No women left behind: Critical leadership development to build gender consciousness and transform organizations, in Handbook of research on gender and leadership year: 2017 ident: pone.0257903.ref007 – volume: 40 start-page: 195 issue: 3 year: 2011 ident: pone.0257903.ref049 article-title: Differential Sensitivity to the Gender of a Person by English and Chinese Speakers publication-title: Journal of Psycholinguistic Research doi: 10.1007/s10936-010-9164-9 – start-page: 447 year: 2015 ident: pone.0257903.ref057 article-title: El morfema género en el pensamiento de la Real Academia Española publication-title: ¿Cuestión que va más allá de la teoría gramatical? – ident: pone.0257903.ref003 – volume: 41 start-page: 7653 issue: 16 year: 2014 ident: pone.0257903.ref020 article-title: Text mining for market prediction: A systematic review publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.06.009 – ident: pone.0257903.ref082 doi: 10.18653/v1/D19-1531 – ident: pone.0257903.ref042 doi: 10.1088/1757-899X/263/4/042062 – year: 2017 ident: pone.0257903.ref076 publication-title: Automated Hate Speech Detection and the Problem of Offensive Language – year: 2014 ident: pone.0257903.ref029 article-title: Research on text classification based on SVM-KNN. in 2014 IEEE 5th publication-title: International Conference on Software Engineering and Service Science – volume: 8 start-page: 12776 issue: 2 year: 2016 ident: pone.0257903.ref041 article-title: An enhanced algorithm for frequent pattern mining from biological sequences publication-title: International Journal of Pharmacy and Technology – volume: 43 start-page: 62 year: 2013 ident: pone.0257903.ref052 article-title: Side Effects of Gender-Fair Language: How Feminine Job Titles Influence the Evaluation of Female Applicants publication-title: European Journal of Social Psychology doi: 10.1002/ejsp.1924 – start-page: 66 year: 2011 ident: pone.0257903.ref045 article-title: The Gendering of Language: A Comparison of Gender Equality in Countries with Gendered, Natural Gender, and Genderless Languages publication-title: Sex Roles – volume: 129 start-page: 131 year: 2018 ident: pone.0257903.ref078 article-title: Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective publication-title: Technological Forecasting and Social Change doi: 10.1016/j.techfore.2017.12.018 – ident: pone.0257903.ref085 doi: 10.1145/3195570.3195580 – year: 2017 ident: pone.0257903.ref037 article-title: A Brief Survey of Text Mining: Classification publication-title: Clustering and Extraction Techniques – volume: 9 start-page: 751 issue: 7 year: 2021 ident: pone.0257903.ref044 article-title: A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets publication-title: Mathematics doi: 10.3390/math9070751 – ident: pone.0257903.ref067 – ident: pone.0257903.ref088 doi: 10.3115/v1/D14-1181 – volume-title: Publication manual of the American Psychological Association: the official guide to APA style year: 2020 ident: pone.0257903.ref012 – volume: 59 start-page: 56 issue: 2 year: 2016 ident: pone.0257903.ref086 article-title: Accountability in algorithmic decision making. Commun publication-title: ACM doi: 10.1145/2844110 – volume: 36 start-page: 20 issue: 1 year: 2019 ident: pone.0257903.ref060 article-title: Comparing automated text classification methods publication-title: International Journal of Research in Marketing doi: 10.1016/j.ijresmar.2018.09.009 – year: 2015 ident: pone.0257903.ref048 article-title: Introducing a gender-neutral pronoun in a natural gender language: The influence of time on attitudes and behavior publication-title: Frontiers in Psychology – year: 2017 ident: pone.0257903.ref009 article-title: Don’t give up on unconscious bias training—Make it better publication-title: Harvard Business Review – volume: 49 start-page: 909 issue: 3 year: 2016 ident: pone.0257903.ref027 article-title: Combining supervised term-weighting metrics for SVM text classification with extended term representation publication-title: Knowledge and Information Systems doi: 10.1007/s10115-016-0924-1 – ident: pone.0257903.ref073 – volume: 44 start-page: 733 issue: 6 year: 2015 ident: pone.0257903.ref050 article-title: Exploring the Cause of English Pronoun Gender Errors by Chinese Learners of English: Evidence from the Self-paced Reading Paradigm publication-title: Journal of Psycholinguistic Research doi: 10.1007/s10936-014-9314-6 – year: 2016 ident: pone.0257903.ref066 publication-title: Reducing gender bias in word embeddings – ident: pone.0257903.ref084 doi: 10.18653/v1/N19-3002 |
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| Snippet | Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in... |
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| SubjectTerms | Age Algorithms Analysis Artificial intelligence Artificial neural networks Bias Biology and Life Sciences Computer and Information Sciences Context Data mining Datasets Demographic aspects Discrimination Dissertations & theses Educational aspects Gender Gender equality Hate speech Human bias Language Linguistic research Linguistics Men Neural networks Physical Sciences Research and Analysis Methods Sentences Sex discrimination Social Sciences Subgroups Texts Theses |
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| Title | An analysis of unconscious gender bias in academic texts by means of a decision algorithm |
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