For a long time, I imagined research as a gradual movement towards certainty. Experiments produced measurements, databases organised evidence, and models turned that evidence into seemingly clear answers. Models, in particular, seemed authoritative: their equations, parameters, and carefully structured calculations suggested exactness and objectivity, even when they described uncertain real-world conditions.

Moving between datasets, disciplinary perspectives, and community engagements challenged this belief. Behind every apparently precise output are decisions about which processes to include, which relationships to simplify, which parameters and assumptions to use, and how to respond when the available data do not fully describe the system.

Much of this learning has taken shape through my PhD research and involvement in CASCADE, a transdisciplinary project that works across five African cities to understand the connections between climate and health risks. Within CASCADE, this has included responsive dialogues and ongoing learning with community participants through Ukomelela Nokufundisana in Khayelitsha, arts-based approaches to translating scientific knowledge about climate and health beyond academic audiences, and learning with different actors through processes such as Learning Labs. 

These spaces have brought different forms of knowledge and experience into conversation and are slowly changing how I think about responsible knowledge production, especially when research findings are intended to inform decisions.

From using knowledge to understanding how it is made

Reading published research or using a model or dataset developed by someone else is one thing. Confronting incomplete observations, inconsistent records, or competing representations to decide how to represent a real process is quite another. 

Working more closely with datasets has made the judgement behind them increasingly visible. Sources describing the same period or event do not always tell exactly the same story. Rainfall datasets, for example, may indicate varying rainfall amounts or levels of extremity for the same period. In compiling flood information, one source may record when and where flooding was reported, while another may provide more detail about the impacts or responses associated with the event. 

This does not necessarily mean that one is right and another wrong. Instead, it requires considering what each dataset represents, its strengths and limitations, and which is most appropriate for the particular question being asked.

Modelling a process involves a series of judgements: how complex the representation needs to be, where to draw system boundaries, which material flows are worth tracking, and how to handle processes too complex to map out completely.  What eventually appears as a clear output is shaped by decisions about how evidence is selected, interpreted and represented.

Moving between disciplines

Working across geology, water engineering, and topics around climate and health has meant learning more than new terminology. Each discipline approaches problems through a different lens, asks different questions, and values different forms of evidence. Engineering may focus on how systems function and perform; climate science may emphasise variability, trends, and extremes; health research may centre exposure, vulnerability, and impacts on people’s lives. 

These perspectives shape how a problem is defined, not just how it is studied. Through CASCADE, I am becoming increasingly aware that transdisciplinary research is not achieved simply by placing different perspectives side by side. It requires translation: understanding what a concept means within each discipline, recognising where apparently similar terms carry different assumptions, and finding ways to connect forms of evidence that were not produced for the same purpose. 

An extreme rainfall event may stand out in climate data. Yet its significance for a health outcome depends on where it occurs, who is exposed, and how existing social and environmental conditions shape its consequences. 

This process of translation also reveals what can be lost when one perspective dominates. A model may represent a process in considerable detail without fully capturing the lived realities that influence how that process unfolds. Community experience may reveal pathways and consequences that remain invisible within available datasets, while quantitative evidence can identify broader patterns that individual experiences alone cannot capture. 

Working across disciplines therefore involves continually asking not only what each perspective contributes but also what it leaves unexplained.

What lived experience makes visible

My involvement in CASCADE community engagements made the limits of disciplinary and analytical perspectives clearest. 

Within scientific literature, a relationship may be represented through a relatively simple pathway: flooding increases the risk of diarrhoeal disease, while boiling potentially contaminated water may be recommended as a protective response. However, in conversations with some of the Khayelitsha residents who experience and respond to these conditions, that apparently simple pathway quickly expands into a much more complex reality.

Boiling contaminated water requires electricity, yet participants described persistent power disruptions during flooding, when access to safe water may be especially important. Similarly, care may be recommended early when severe diarrhoea is suspected, but long queues, limited clinic availability, and repeated referrals can delay access to care. 

This shows that evidence may point to what works in principle, yet real-world settings are far more complex than the literature or the data suggest.  Community engagement challenges our assumptions by showing why the same event can lead to different outcomes in different places. It also reveals how people view the exact same issue differently based on their unique roles, experiences, and access to information.

Rather than seeing scientific evidence and lived experience as competing sources of knowledge, I see value in bringing them together to develop a more contextual understanding of both the problem and what responses may actually be possible.

From producing evidence to supporting decisions

Researchers produce detailed datasets and models, yet this evidence rarely leads to direct action. Decision-makers are constrained by budgets and institutional mandates, while residents with urgent needs often lack the influence to direct resources.

This raises questions that scientific research alone does not always address. Who needs to receive the evidence? In what form would it be useful? At what stage should different actors, including affected communities, be involved? What institutional relationships are needed for knowledge to move beyond a report, publication or workshop? These questions broaden what might count as a successful research output. Accuracy remains important, but so do accessibility, relevance and timing.

From certainty to honesty 

Moving between data, different fields, and the lived experiences of people facing climate and health risks has fundamentally broadened how I view research. While I once believed good research meant finding the most certain answer, I now see it as recognising the limits of our methods and staying open to other forms of knowledge.

Data and models are essential, but lived experience reveals the practical constraints, priorities, and realities that science alone cannot see. Therefore, strong research requires transparency about our assumptions, methods, and limitations. 

About the Author

Christina is a doctoral student at the Water Research Group at the University of Cape Town, with a background in Geology (Honours) and Water Quality Engineering (Masters). Her PhD focuses on laboratory experimentation and predictive modelling for wastewater sludge valorisation using catalysed hydrothermal carbonisation (CHTC). In parallel, Christina contributes to the transdisciplinary CASCADE project at the Climate Systems Analysis Group (CSAG), exploring urban climate-health risks across African cities.

By Christina Mazivila