Dictionary Series: What do we mean when we talk about multi/inter/trans disciplinary?


As an organisation that prides itself in collaboration, just how collaborative are we, and how can that be defined?

 

What do we mean when we talk about (insert prefix of your choice) multi/inter/transdisciplinary?

By Alex Hutchison, Director, Data for Children Collaborative

In what are apparently interchangeable terms in some usage contexts, it strikes me that given how often we talk about how we work across disciplines and bring disciplines together, perhaps it was time to pin our colours to the mast. As such, of course, I went to Google to see what they thought. And here are some of their stone cold definitions:

Multidisciplinary – “combining or involving several academic disciplines or professional specializations in an approach to a topic or problem”.

Interdisciplinary – “relating to more than one branch of knowledge”.

Transdisciplinary – “defined as research efforts conducted by investigators from different disciplines working jointly to create new conceptual, theoretical, methodological, and translational innovations that integrate and move beyond discipline-specific approaches to address a common problem”.

It looks to me like transdisciplinary is the winner of that little definition battle!

On reflection, that indeed should be the case. As a good Latin scholar (yes, I did a Latin A-level, and yes, I do find myself dusting off that knowledge from time to time), I would view these three terminologies by translating them directly to multi = many, inter = between, trans = across. By that logic, ‘transdisciplinary’ does seem to be the best summary of the construct that we are intent on mining at the Data for Children Collaborative. The intention of bringing disciplines together is not just to walk the walk but to truly create something innovative by bringing diverse minds together. Of course, at the Collaborative, we then extend the collaborations to work across sectors and geographies, too, to really bring together an eclectic mix of thoughts and expertise.


How do we practice transdisciplinary approaches in earnest?

Intrinsic to our approach to pulling together the right team for the challenge, our practices ensure that we are non-specific about the domains that we are looking to involve. The deconstruction of the Challenge Question, down to skills sets rather than in terms of a solution, is a critical part of the process. We also ensure that the mechanics of engagement and procurement are unbiased, having a route in for all. Above all, we co-create the project with all the different prospective delivery partners (as well as the challenge owner) to ensure that we are bringing in that diversity of thought/approach/context from the outset.

Additionally, we spend time at the beginning of the project, when on-boarding the different collaborators, to truly understand their incentive for being involved. We all have different reasons for doing what we do, and that is to be expected. The key is identifying them, being sure that they fit with the broader mission, and then continually considering individual and organisational influences as a project ebbs and flows.


What are the enablers and the challenges to transdisciplinary practice?

We are three years into our learning journey at the Data for Children Collaborative (and we have by no means finished learning). So far, we see the following as key components to a successful collaborative venture:

Purpose

This refers to purpose through a number of lenses. Firstly, there’s our organisational purpose at the Data for Children Collaborative and the fact that we need to be clear about this to any potential partners so that we are not swayed from our mission. Then there is the purpose of the specific Challenge Question itself and the importance of somebody actually asking this question such that an answer will be put to use and ultimately benefit children. Finally, there is the purpose or the motivator of the partners getting involved, as mentioned above. The purpose is both an enabler and a challenge to transdisciplinary working, in such that with it clearly stated for all, everyone can move forward in a collective direction. While if the purpose is going to crash into incentives or motivations at all, then things can start to unravel.

Respect

Respect also comes in a variety of hues in the context of transdisciplinary work. There needs to be an overarching respect for the different domains, contexts, and expertise engaged in the work. This extends beyond the core delivery team but across the wide and varied stakeholders that this work has an impact on. There needs to be respect for the variety of roles and responsibilities across the team. Our experience tells us that minimising hierarchies will enable mutual respect more sincerely. Respect is built at the beginning of a team forming but needs to be maintained throughout the duration of the project, particularly when tougher decisions and priority calls need to be made.

Decision Making

As advocates for co-creation and collaborative problem-solving, we love to hear lots of opinions, ideas and perspectives on the bigger picture and the minute details. We know we are not the experts, and as such, we want to hear from lots of different experts to get a well-rounded set of solutions. But, and this is a big but, there has to be decision-making to make progress. Clearly defined roles and responsibilities help empower team members to make decisions without being stagnated by seemingly democratic debates. On top of this, providing a clear rationale for the selection of a particular decision supports both transparency and diplomacy. Decisions are what make the world go round or at least keep a project moving on a forward trajectory.


To conclude

The more I read the definition for transdisciplinary – “research efforts conducted by investigators from different disciplines working jointly to create new conceptual, theoretical, methodological, and translational innovations that integrate and move beyond discipline-specific approaches to address a common problem” – the more I’m kicking myself not using this word exclusively to describe our approaches over the last three years!! Lesson learned.

 
 
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Dictionary Series: What do we mean when we talk about Natural Language Processing (NLP)?