Dataset Information

The gendered nature of authorship.

ABSTRACT: Authorship is the primary form of symbolic capital in science. Despite this, authorship is rife with injustice and malpractice, with women expressing concerns regarding the fair attribution of credit. Based on an international survey, we examine gendered practices in authorship communication, disagreement, and fairness. Our results demonstrate that women were more likely to experience authorship disagreements and experience them more often. Their contributions to research papers were more often devalued by both men and women. Women were more likely to discuss authorship with coauthors at the beginning of the project, whereas men were more likely to determine authorship unilaterally at the end. Women perceived that they received less credit than deserved, while men reported the opposite. This devaluation of women’s work in science creates cumulative disadvantages in scientific careers. Open discussion regarding power dynamics related to gender is necessary to develop more equitable distribution of credit for scientific labor.

SUBMITTER: Ni C

PROVIDER: S-EPMC8442765 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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The gendered nature of authorship.

Ni Chaoqun C Smith Elise E Yuan Haimiao H Larivière Vincent V Sugimoto Cassidy R CR

Science advances 20210901 36

Authorship is the primary form of symbolic capital in science. Despite this, authorship is rife with injustice and malpractice, with women expressing concerns regarding the fair attribution of credit. Based on an international survey, we examine gendered practices in authorship communication, disagreement, and fairness. Our results demonstrate that women were more likely to experience authorship disagreements and experience them more often. Their contributions to research papers were more often ...[more]

PMID: 34516891

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Project description:Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (lexical, morphological, and structural) relevant to Albanian and experiment with multiple classifiers using two different multiclass classification strategies. Furthermore, we compare our results to those obtained using deep learning models. Our investigation focuses on identifying the best combination of features and classification methods. The results reveal that lexical features are the most effective set of linguistic features, significantly improving the performance of various algorithms in the authorship attribution task. Among the machine learning algorithms evaluated, XGBoost demonstrated the best overall performance, achieving an F1 score of 0.982 on literary works and 0.905 on newsroom columns. Additionally, deep learning models such as fastText and BERT-multilingual showed promising results, highlighting their potential applicability in specific scenarios in Albanian writings. These findings contribute to the understanding of effective methods for authorship attribution in low-resource languages and provide a robust framework for future research in this area. The careful analysis of the different scenarios and the conclusions drawn from the results provide valuable insights into the potential and limitations of the methods and highlight the challenges in detecting authorship in Albanian. Promising results are reported, with implications for improving the methods used in Albanian authorship attribution. This study provides a valuable resource for future research and a reference for researchers in this domain.

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The gendered nature of authorship.

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The gendered nature of authorship.

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