N.B. Gender in this blog means gender binary.
A few weeks ago, I was a typing a text to a friend. As I typed the word ‘emotional’, the ‘Loudly Crying Face’ emoji came up in the predictive text. I found it intriguing that ‘a global language’, with approximately 79 visual representations of emotions, selected crying to represent the feeling of emotions. I decided to ask a few friends which emoji came up when they typed the word ‘emotional.’ Out of a very small random sample of five, the same emoji came up.
What is interesting is not the emoji itself nor its association with the word emotional but the association when placed within the context of gendered identities. The act of crying, as a human emotion, is typically associated with femininity and to associate emotion (a cognitive and physiological body function) with crying produces a type of knowledge about the world; to be emotional is to be feminine.
Crying as an emoji is not exclusively associated with femininity but the association is still gendered. This association redefines crying as a masculine or feminine act depending on the nature of the space and whether it is public or private. Imagine that the arrows in the diagram below are either public/private. In the public domain such as politics and war, crying takes a masculine identity (i.e. war cry, rally cry, outcry). In the private domain, such as the expression of human emotion, crying is associated with the construction of femininity. The big cog, turning everything is gender.
Discourse is particularly powerful because it frames the way we process reality, even something as ‘objective’ as data. The act of crying and the state of feeling emotional are two entities, representing two realities in our world. However, the use of the Loud Crying Face emoji to represent the word ‘emotional’, is a type of information about the world that has been processed through a certain context (gender).
To illustrate how gender bias facilitates association, I decided to type the following two phrases on Google Search and see what the most common search terms were. Again, this is not a robust scientific experiment but to illustrate just how powerful discourse is in constructing our perception of the world. The process of selecting certain types of realities as data (information points), the process of processing these data as knowledge about the world (information), are situated within contexts (cognitive biases), one of these contexts being gender.
The quest for a gender agenda in the use of data for development policy revolves around two key strategies; more robust data on women and more disaggregated data to measure differences in the effects of poverty between the binary sexes. An example of this is the Data2x research agenda launched by UN Foundation. The availability of data, the process of being made visible, is pro-poor because it allows for the creation of policies that target and address the conditions people living in poverty face. Poor public policy infrastructure prevents the collection and monitoring of the population’s needs, including women, who are typically over-represented in invisible spaces; the informal economy, the household and within marginalised geographies (i.e. rural areas, slums etc).
Yet there is another debate, in the philosophy of knowledge field, which has been side-lined from the Sustainable Development Agenda and asks an important question – How do we know that what we know is not a gendered construction of what exists?
This is what the field of feminist epistemology, specifically feminist empiricism, proposes. They argue that the way we seek to know the world is informed by cognitive biases which influence what, how and why we choose specific types of realities as data/information/knowledge/evidence about the world. This very broad and diverse knowledge community do not challenge that there are observations of the world which exist objectively. What they challenge is the objectivity in our ways of knowing.
Contrary to the etymology of the word data (to have been given), feminist epistemologists believe data take from this world. That is to say, the process of creating knowledge is active in that it is political, it is political in that it is an active process. Data and knowledge are not only political processes but political commodities.
A prominent example is the field of biodiversity in international development, once viewed as an objective, rational and scientific investigation of our relationship with natural ecosystems. The biodiversity field has developed to explore the ways in which previous knowledge of the field did not count women’s work in, women’s relationship with and women’s access to biodiversity.
This is an important line of inquiry because it has implications for the way we use data to achieve gender equality outcomes in the Sustainable Development Goals (SDGs). The United Nations Statistical Commission established the High-level Group for Partnership, Coordination and Capacity-Building for the 2030 Agenda (HLG-PCCB) which will develop new strategies to improve national statistical capacities to monitor the Sustainable Development Goals. This High-Level Group recently developed the Global Action Plan for Sustainable Development Data focusing on six strategic objectives to build the statistical capacities of member states. Under Objectives 3.5 of the plan, ‘Strengthen and expand data on domains that are currently not well developed within the scope of official statistics’, lies a beacon of hope. One of the key actions proposed under this objective is for member states to ‘Advance the construction of concepts and methodologies to obtain indicators that are more difficult to measure.’ I see this as an opportunity for feminist epistemologists to contribute to the development community’s expanding definition of progress.
In the Sustainable Development Agenda, we have revised our concept of development to include environmental as well as social, economic and political development. We revised our approach to policy consultations in the making of this agenda leading to the most comprehensive and far-reaching agenda-setting process the world has undertaken. Despite this, the approaches we adopt to inform our knowledge of progress have been interrogated in practice but not in theory.
How can we use the current momentum for gender responsive data to inform a theoretical revision of our ways of knowing in development? How can we pursue new/alternative ways of knowing the world which explicitly acknowledge the complex ways gender interacts with this process?