Gender Data 4 Girls?: A Postcolonial Feminist Participatory Study in
Bangladesh
- URL: http://arxiv.org/abs/2108.10089v1
- Date: Mon, 23 Aug 2021 11:41:27 GMT
- Title: Gender Data 4 Girls?: A Postcolonial Feminist Participatory Study in
Bangladesh
- Authors: Isobel Talks
- Abstract summary: Postcolonial feminism remains underutilised for critically investigating data for development projects.
This paper presents the findings from a participatory action research project with young women involved in a gender data for development project in Bangladesh.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Premised on the logic that more, high-quality information on majority world
women's lives will improve the effectiveness of interventions addressing gender
inequality, mainstream development institutions have invested heavily in gender
data initiatives of late. However, critical empirical and theoretical
investigations into gender data for development policy and practice are
lacking. Postcolonial feminist theory has long provided a critical lens through
which to analyse international development projects that target women in the
majority world. However, postcolonial feminism remains underutilised for
critically investigating data for development projects. This paper addresses
these gaps through presenting the findings from a participatory action research
project with young women involved in a gender data for development project in
Bangladesh. Echoing postcolonial feminist concerns with development, the
'DataGirls' had some concerns that data was being extracted from their
communities, representing the priorities of external NGOs to a greater extent
than their own. However, through collaborating to develop and deliver community
events on child marriage with the 'DataGirls', this research demonstrates that
participatory approaches can address some postcolonial feminist criticisms of
(data for) development, by ensuring that gender data is enacted by and for
majority world women rather than Western development institutions.
Related papers
- Women's Participation in Computing: Evolving Research Methods [0.0]
A 2022 keynote for the ACM History Committee on "Why SIG History Matters: New Data on Gender Bias in ACM's Founding SIGs 1970-2000"
presented new data describing women's participation as research-article authors in 13 early ACM Special Interest Groups.
This report expands on these earlier articles, and their evolving research method, connecting them to the ACM SIG Heritage presentation.
arXiv Detail & Related papers (2024-07-25T00:05:18Z) - Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology [22.458957168929487]
Existing research in measuring and mitigating gender bias predominantly centers on English.
This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi.
arXiv Detail & Related papers (2024-05-10T09:26:12Z) - "I'm fully who I am": Towards Centering Transgender and Non-Binary
Voices to Measure Biases in Open Language Generation [69.25368160338043]
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation.
We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
arXiv Detail & Related papers (2023-05-17T04:21:45Z) - Monitoring Gender Gaps via LinkedIn Advertising Estimates: the case
study of Italy [3.5493798890908104]
We evaluate the potential of the LinkedIn estimates to monitor the evolution of the gender gaps sustainably.
Our findings show that the LinkedIn estimates accurately capture the gender disparities in Italy regarding sociodemographic attributes.
At the same time, we assess data biases such as the digitalisation gap, which impacts the representativity of the workforce in an imbalanced manner.
arXiv Detail & Related papers (2023-03-10T11:32:45Z) - "STILL AROUND": Experiences and Survival Strategies of Veteran Women
Software Developers [53.5211430148752]
We conducted 14 interviews to examine the experiences of people at the intersection of ageism and sexism.
We identified 283 codes, which fell into three main categories: Strategies, Experiences, and Perception.
Several strategies we identified, such as (Deliberately) Not Trying to Look Younger, were not previously described in the software engineering literature.
arXiv Detail & Related papers (2023-02-07T19:26:15Z) - Towards Understanding Gender-Seniority Compound Bias in Natural Language
Generation [64.65911758042914]
We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models.
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
arXiv Detail & Related papers (2022-05-19T20:05:02Z) - The effect of the COVID-19 pandemic on gendered research productivity
and its correlates [0.0]
This study examined how the proportion of female authors in academic journals on a global scale changed in 2020.
We observed a decrease in research productivity for female researchers in 2020, mostly as first authors, followed by last author position.
Female researchers were not necessarily excluded from but were marginalised in research.
arXiv Detail & Related papers (2021-11-29T06:20:44Z) - For Better or for Worse? A Framework for Critical Analysis of ICT4D for
Women [0.0]
As ICT diffusion widens, there is a persistent threat of widening the gender-based digital divide.
This paper develops a critical research framework for a gender-focused examination of ICT4D studies.
arXiv Detail & Related papers (2021-08-23T05:42:24Z) - How True is GPT-2? An Empirical Analysis of Intersectional Occupational
Biases [50.591267188664666]
Downstream applications are at risk of inheriting biases contained in natural language models.
We analyze the occupational biases of a popular generative language model, GPT-2.
For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity.
arXiv Detail & Related papers (2021-02-08T11:10:27Z) - Learnings from Frontier Development Lab and SpaceML -- AI Accelerators
for NASA and ESA [57.06643156253045]
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines.
We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA.
FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
arXiv Detail & Related papers (2020-11-09T21:23:03Z) - Young Adult Unemployment Through the Lens of Social Media: Italy as a
case study [108.33144653708091]
We employ survey data together with social media data to analyse personality, moral values, but also cultural elements of the young unemployed population in Italy.
Our findings show that there are small but significant differences in personality and moral values, with the unemployed males to be less agreeable.
Unemployed have a more collectivist point of view, valuing more in-group loyalty, authority, and purity foundations.
arXiv Detail & Related papers (2020-10-09T10:56:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.