Knowledge production flow on iNaturalist

Knowledge production flow on iNaturalist

(Wondering how to add your blog posts to this blog? Have a look here: Get involved)

What platform are we talking about?

iNaturalist is a a citizen science online platform where anyone can share and identify observations of wild animals and plants.

What is a knowledge production flow?

A knowledge production flow summarizes the basic steps users take when interacting with the platform in order to produce knowledge. The steps were identified using a method similar to a cognitive walkthrough. A cognitive walkthrough is a usability inspection method focussing on a user’s problem solving process, assuming exploration of the website as the main learning strategy. In our case, the focus was not on usability, but on understanding the knowledge creation process and on the existence of peer production elements. Modeling the knowledge production process in this way helps to identify how users interact with the platform and if and how they collaborate during their work - an important step to see if and how peer production takes place on the respective platform, and if there are ways to increase peer production if wanted.

How does knowledge production work on iNaturalist?

A basic knowledge production flow on iNaturalist (see Figure 1) can start with a user making observations of living organisms that they are interested in. The context might be individual walks, or community data collection events – named “Bioblitzes” – or other user-generated data collection projects. The interaction with iNaturalist starts with the user uploading their observation to the platform, providing a guess on the species. As a next step, other users participate in a communal validation process by adding their guess of the species, until the observation is labelled as research grade. Research grade observations then go into the scientific GBIF (Global Biodiversity Information Facility) database. Users are free to only take part in either the uploading or the identification of species.

A 
  knowledge production flow consisting of four steps: 1) Preparation (a user makes wildlife observations in nature), 2) Open knowledge production on the platform
  (2.1) User uploads observation, 2.2) Species identification process by community), 3) Shared research artifact (data goes into biodiversity database)
Figure 1: Visualization of a basic knowledge production flow on iNaturalist

Thoughts

While knowledge production on iNaturalist is quite straightforward, users have a certain amount of creative freedom (e.g. choosing which kind of observations they are interested in, how much, when and where they want to upload, if they rather identify or upload, or if they want to start or participate in a data collection project). They are also free to use the data for their own purposes, even though data analysis is not part of the platform itself. Apart from the collaborative creation of a biodiversity database that consists of all the observations that users uploaded, there is a more direct form of collaboration in their communal validation process (click here to read more about communal validation on iNaturalist).

References

On usability and cognitive walkthroughs

  • Hollingsed, T., Novick, D.G., 2007. Usability inspection methods after 15 years of research and practice. In: Proceedings of the 25th Annual ACM International Conference on Design of Communication - SIGDOC ’07. Presented at the the 25th annual ACM international conference, ACM Press, El Paso, Texas, USA, pp. 249-255. DOI: https://doi.org/10.1145/1297144.1297200
  • Nielsen, J., 1994. Usability Inspection Methods. Presented at the Conference Companion on Human Factors in Computing Systems, pp. 413–414.
  • Wharton, C., Rieman, J., Lewis, C., Polson, P., 1994. The cognitive walkthrough method: A practitioner’s guide. In: Usability Inspection Methods. pp. 105–140.

Katharina
Katharina

PhD student at CRI (Center for Research and Interdisciplinarity) in Paris, experimenting with a user-centered approach to support the peer-production of knowledge in citizen science.

comments powered by Disqus