Disciplinary diversity widget: how does your team measure up?

Community member post by Brooke Struck

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Brooke Struck (biography)

Would it be useful to have a tool to quickly measure the disciplinary diversity of your team? At Science-Metrix we’ve created a widget for just such a purpose. In this post, I’ll explain what the disciplinarity widget does, how to use it, how to interpret the measurements and how we are refining the tool.

How is disciplinary diversity measured?

For several years, Science-Metrix has maintained a classification of research into a three-level taxonomy, arranging research into domains, fields and subfields. We have also developed several approaches to assess the conceptual proximity of these subfields to each other, based on how often material from these subfields is used in combination.

With the taxonomy in hand, and a proximity matrix relating the subfields to each other, we can calculate disciplinary mix using a three-dimensional approach. The first dimension is simply the number of different subfields integrated, the second dimension is the balance between the subfields being represented, and the third dimension is the conceptual distance between them.

For example, a team that consists of five biologists and one chemist is considered less diverse than a team of three biologists and three chemists, because the latter team is more balanced between the subfields involved. Similarly, a team with five biologists and one chemist is considered less diverse than a team with five biologists and one performing artist, because biology and chemistry are conceptually more proximate to each other than are biology and the performing arts.

How do you use the widget?

Using the widget is intended to be very simple. Each team member needs to be tagged for the relevant subfield that they represent. In order to collect this information, the widget asks you to name your team and identify the sector in which you’re working, and it then presents you with a menu to navigate through our three-level taxonomy and identify your subfield.

Once you’ve inputted your own information, a link is provided for information to be supplied for your teammates as well—a link that you can send to your teammates, or that you yourself can click through in order to enter information on their behalf.

How do you know what subfield to associate yourself with? At Science-Metrix, we generally recommend using your highest level of education (or the degree most recently completed, as a tiebreaker). However, in some cases, people have ventured into completely new intellectual areas since finishing their studies, so it is perhaps more relevant for them to identify their new area of expertise instead.

For now, each person can only choose one subfield to represent themselves in the measurement. If you find this particularly constraining, let us know, as allowing multiple subfields per person is a feature we can consider building if this challenge is widespread.

What do the results mean?

The scores reflect:

  • Number of sub-fields represented
  • Balance between the disciplines represented
  • Intellectual distance between the subfields represented.

Scores on this indicator range from 0 to 1, 0 being completely monodisciplinary and 1 being maximally diverse. The ranges of these scores can be interpreted as follows:

  • 0 means totally disciplinary, everyone from the same background.
  • 0.1–0.2 is a low score, meaning that there is one “home” discipline with a few “secondary” areas also included.
  • 0.3–0.5 is a mid-range score, meaning that there is a balance amongst the disciplines represented but that they’re all still quite clustered in one intellectual area.
  • 0.6 and above is a high score, meaning that several different disciplines are involved, they’re relatively balanced (rather than a “home and guest” model), and they’re drawn from a broad intellectual diversity.

Collecting widget data

We’re collecting anonymized data through the widget to see how diverse the teams are out there, what kinds of disciplinary combinations might crop up, and so forth. If we can characterise patterns broadly enough, they can contribute valuable information for users in interpreting their own scores.

The main data we collect are the disciplinary diversity scores, which we’ll be able to slice by sector (inputted manually) and by geographic area (collected via the Internet Protocol (IP) address stamped on the submission). We’ve left a space for you to input your email if you’d like to receive updates about new features built into the widget, and findings that we’ve uncovered looking at patterns in the inputted data. We designed the widget to log your email independently from your team’s data.

Widget development: Seeking feedback!

We’re currently working to improve the widget, so we’re looking for feedback! At a User Experience (UX) event in Montreal in early 2018, audience members commented on the smooth workflow of the tool, but pointed out that we’ve rolled together two distinct functions:

  • Calculation: to measure the score of an existing team
  • Exploration: to experiment with various permutations of potential teams.

Data collected to date bears out this remark as about half the teams entered have the word “Test” in their name.

What’s the value to you of knowing about disciplinary diversity? If you’ve tried the widget, is there something about the tool that feels clunky or unintuitive to use? Is there another tool that you’d like to see this widget integrate with, like maybe your Open Researcher and Contributor ID (ORCID) (and the ORCIDs of your colleagues)?

Click here to access the disciplinarity widget. You can email feedback to me directly (brooke.struck@science-metrix.com).

This blog post is adapted from a longer version “Team diversity widget: how do you measure up?” which appeared on the Science-Metrix blog: http://www.sciencemetrics.org/team-diversity-widget-measure/

Biography: Brooke Struck is a senior policy officer at Science-Metrix Inc. in Montreal, Canada, where his role includes contributing to project design and management, research and analysis, reporting, and communication with clients. He also leads research projects for the development of new indicators to measure scientific activity. These projects integrate research from the history and philosophy of science with emerging policy priorities and bibliometric innovations to ensure that new indicators are both strategically relevant to client needs and methodologically robust. Additionally, he contributes synthetic and critical assessments of science governance and policy developments through the ScienceMetrics.org blog.

Foundations of a translational health sciences doctoral program

Community member post by Gaetano R. Lotrecchiano and Paige L. McDonald

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Gaetano R. Lotrecchiano (biography)

How can doctoral studies be developed to include innovation in practice and research, as well as systems and complexity thinking, along with transdisciplinarity? This blog post is based on our work introducing a PhD in Translational Health Sciences at George Washington University in the USA.

Innovation in Practice and Research

We suggest that innovation in practice and research is achieved by the integration of knowledge in three key foundational disciplines:

  • translational research
  • collaboration sciences
  • implementation science (Lotrecchiano et al., 2016).

We define these as follows:

Translational research is a crosscutting approach that informs associations across a continuum of knowledge generation from basic biomedical discovery to rehabilitation interventions to global population health impact.

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Paige L. McDonald (biography)

Collaboration sciences form the foundation by which translational research is conducted and when implemented along with practice and policy efforts ensure that translational science can occur with strong representation of multi-stakeholders invested in health outcomes.

Implementation science is the investigation of processes and strategies influencing the movement of evidence-based healthcare and prevention strategies or programs from the clinical or public health knowledge base into routine use.

When considered together, these provide a recipe for high impact in innovations research and practice (see figure below from Lotrecchiano et al., 2016).

These three disciplines support innovations in health practices and research necessary to promote changes at the organizational, team and individual levels. All three are reflected in the overall program goals and were used to inform curriculum competencies. The aim is to prepare students to move from more basic approaches to research to those that are more systems based, as shown in the following figure (from Lotrecchiano et al., 2016).

 

 

Pairing complexity principles with transdisciplinary characteristics

Moving to a more systems-based approach requires the pairing of complexity principles with transdisciplinary characteristics to develop scientists equipped to operate beyond the confines of traditional or unidisciplinary training. These are illustrated in the table below, with complexity principles in the left hand column and transdisciplinary characteristics in the right hand column. We feel that introducing doctoral students to these principles allows them to participate in translational trandisciplinary research activities.

Complexity principles (left hand column) and transdisciplinary characteristics (right hand column) –  full references are available in Lotrecchiano (2012)

Conclusion

Readers may be interested in our doctoral student handbook (PDF 1.1MB). Our work to establish and maintain our approach to doctoral studies in this vein continues and we have enjoyed both successes and setbacks, but mostly successes, as we transform the way we approach this particular type of doctoral training amidst the healthcare and research climate in the United States.

We invite your comments and questions and hope to hear from you about your experiences.

To find out more:
Lotrecchiano, G. R., McDonald, P. L., Corcoran, H. K. and Ekmekci, O. (2016). Learning Theory, Operative Model, and Challenges in Developing a Framework for Collaborative Translational and Implementable Doctoral Research. Conference proceedings, 9th Annual International Conference of Education, Research and Innovation, 14-16 November, 2016, Seville: Spain. Online via Researchgate – 311363970

Reference:
Lotrecchiano, G. R . (2012). Social Mechanisms of Team Science: A Descriptive Case Study Using a Multilevel Systems Perspective Employing Reciprocating Structuration Theory. Doctoral dissertation, George Washington University: Washington DC United States of America. Online: https://pqdtopen.proquest.com/pqdtopen/doc/992950947.html?FMT=ABS

Biography: Gaetano R. Lotrecchiano, EdD PhD is an Associate Professor at the George Washington University (GWU) School of Medicine and Health Sciences, Washington DC USA, where he is the Director of Doctoral Candidacy in the PhD in Translational Health Sciences Program. He is the vice-president of the International Society for Systems and Complexity Sciences for Health and of the International Society of the Science of Team Science. He is the convener of the GWU program entitled Creating a Culture of Collaboration at GWU. He is also the Team Science Lead of the Clinical and Translational Science Institute (CTSI-CN), a partnership between Children’s National Health System and George Washington University.

Biography: Paige L. McDonald, EdD is an Assistant Professor at the George Washington (GW) University School of Medicine and Health Sciences, Washington DC USA, where she is the Director of Curriculum in the PhD in Translational Health Sciences Program. She is the Managing Director for the GW IMPACT Initiative and GW Collaboratory for Health Research and Education. She is also the Secretary of the International Society for Systems and Complexity Sciences for Health.

How is transformative knowledge ‘co-produced’?

Community member post by Andy Stirling, Adrian Ely and Fiona Marshall

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Andy Stirling (biography)

It’s often said that knowledge to tackle big problems in the world – food, water, climate, energy, biodiversity, disease and war – has to be ‘co-produced’. Tackling these problems is not just about solving ‘grand challenges’ with big solutions, it’s also about grappling with the underlying causal social and political drivers. But what does co-production actually mean, and how can it help to create knowledge that leads to real transformation?

Here’s how we at the Social, Technological and Environmental Pathways to Sustainability (STEPS) Centre approach this challenge of co-production. Continue reading

Interdisciplinarity and evil – Understanding incommensurability

Community member post by J. Britt Holbrook

J. Britt Holbrook (biography)

Incommensurability is a recognized problem in interdisciplinary research. What is it? How can we understand it? And what can we do about it?

What is it?

Incommensurability is best illustrated by a real example. I once co-taught a class with a colleague from another discipline. Her discipline depends on empirical analysis of data sets, literally on counting things. I, on the other hand, am a philosopher. We don’t count. One day she said to our students, “If you don’t have an empirical element in what you’re doing, it’s not research.” I watched the students start nodding, paused for half a beat, and volunteered, “So, I’ve never done any research in my entire career.” “That’s right!” she replied, immediately, yet hesitating somewhere between a discovery and a joke. Continue reading

Lessons from “real-world laboratories” about transdisciplinary projects, transformative research and participation

Community member post by Antonietta Di Giulio and Rico Defila

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Antonietta Di Giulio (biography)
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Rico Defila (biography)

In Germany there has recently been a heated debate about the need for, and the justification of, so-called “transformative research”. At the same time, German funders are increasingly supporting research in “real-world laboratories” and these explicitly aim to bring about social change. We lead an accompanying research project (“Begleitforschung” in German) in a real-world laboratory program of research in Baden-Württemberg (see Schäpke et al., (2015) for more information). This has led us to reflect upon the relationship between transdisciplinary research and transformative research, and how this impacts on how we think about participation in research. We share some preliminary ideas here.
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Doing a transdisciplinary PhD? Four tips to convince the examiners about your data

Community member post by Jane Palmer, Dena Fam, Tanzi Smith and Jenny Kent

How can research writing best be crafted to present transdisciplinarity? How can doctoral candidates effectively communicate to examiners a clear understanding of ‘data’, what it is and how the thesis uses it convincingly?

The authors have all recently completed transdisciplinary doctorates in the field of sustainable futures and use this experience to highlight the challenges of crafting a convincing piece of research writing that also makes claims of transdisciplinarity (Palmer et al., 2018). We propose four strategies for working with data convincingly when undertaking transdisciplinary doctoral research.

1. Make the data visible and argue for the unique or special way in which the data will be used

Some of the comments received from our examiners reflected a sense of being provided with insufficient data, or that it was not convincing as data.

It is important that the nature of data for the purposes of the research is clearly defined, and presented in a way that demonstrates its value in the research process. Richer contextualization of the data can help to make clear its value. This can include drawing attention to the remoteness of the field location, the rare access gained to the participants, and/or the unusual or special qualities of the data that make an original contribution to knowledge.

In these and other cases, it may be important to explain how a particular kind of data can valuably inform an argument qualitatively without reference to minimum quantitative thresholds. This is particularly relevant where a transdisciplinary doctoral candidate is crossing between physical/natural science, humanities and social science disciplines.

2. Be creative and explore the possibilities enabled by a broad interpretation of ‘data’

The advantage conferred on the candidate in taking a transdisciplinary approach needs to be made evident to the examiners, especially where there may appear to have been an absorption of the ‘data’ in the wider synthesizing narratives that are typical of transdisciplinary writing.

Adopting more creative writing techniques may help the examiner both to see the data, and to see the research as valuable. Transdisciplinary doctoral candidates may, given the complex feat of communication this requires, find it useful to seek training in creative writing or science communication skills.

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Jane Palmer (biography)

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Dena Fam (biography)

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Tanzi Smith (biography)

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Jenny Kent (biography)

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