This project is maintained by amartrics
My central research question for this project concerns the mapping and tracking of gender-disaggregated economic & health phenomena in Southeast Asia. Specifically, I hope to provide a semblance of an answer to the following inquiry: how can data scientists begin to quantify gender discrimination beyond a qualitative scale, and what enumerative indicators currently recorded by info-rich, non-distinct databases could be most useful to this process? In addition to investigating this query, I would also like to delve deeper into the issue of specialization in terms of data collection, especially considering the significant lack of gender-focused raw data. I believe that utilizing an explanatory approach to understand the causes and effects of gender discrimination as a whole would offer a holistic examination of this problem. Beyond connecting the dots between incidents of, for instance, domestic violence and patriarchal norms, I would like to try to determine the incubatory factors that lead to the development of these norms – the cause behind the causes, so to speak. Taking some influences from a course of descriptive inquiry could also be helpful over the course of this research, allowing for the profiling of populations in identifying levels of success regarding gender equality. I feel I have already spent a lot of time focusing on the region of Southeast Asia; by synthesizing data mined from the well-curated data pools of “developed” countries with predictive modelling, I believe I could find a great way to answer my central research question, or at least provide a hypothetical, scientifically-founded method of doing so through comparative analysis.
Some of the sub-CRQs I am currently considering include queries about data collection and rates of comparison. How does the lack of women involved in information & communications technology, especially in Southeast Asia, impact the collection, detection of bias, and publication of open data? Is there a way to examine the transparency or viability of data between gender-inclusive and -exclusive data projects? How truly effective are global initiatives for the advancement of women in tech and data industries, especially in areas where there is already stiff opposition to women’s involvement in non-gender-specific lines of employment? I hope to get the chance to examine at least some of these questions in depth while seeking to analyze my central research question.