Soft skills and hard numbers
Gender discourse in human resources
Bibliographic Data
| ID | 5260610 |
|---|---|
| Authors | R Hong (0000-0002-2126-0384, University of Southern California, corresponding author) |
| Year | 2016 |
| Volume | 3 |
| Issue | 2 |
| Publication date | 2016-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2053951716674237 |
| OpenAlex | W2530447657 |
| Language | EN |
| Citations received | 10 |
| References cited | 32 |
The cultural rise of "big data" in the recent years has pressured a number of occupations to make an epistemological shift toward data-driven science. Though expressed as a professional move, this article argues that the push incorporates gendered assumptions that disadvantage women. Using the human resource occupation as an example, I demonstrate how normative perceptions of feminine "soft skills" are seen as irreconcilable with the masculine "hard numbers" of a data-driven epistemology. The history of human resources reflects how assumptions of a biological fit with an occupation limit what women can convincingly describe as her skillsets. However, challenging this cannot stay within the confines of the occupation itself. To undo sexist thinking, it is necessary to understand the broader networks of patriarchal power that dictate how value is defined in corporate environments, especially within other high status professions in business
Disadvantage · Epistemology · Normative · Political science · Sociology · Undo · Computer Science · Gender Diversity and Inequality · Law · Management and Organizational Studies · Sex and Gender in Healthcare
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| Unique citing works | 10 |
|---|---|
| Citations per year | 1,43 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |