Transboundary E-waste Main Menu Introduction: a map of the map. An introductory page for users after the landing page. Defining a starting point for the controversy map. A description of how we obtained a floating statement for the controversy map. Mapping the controversy on the web. A path containing the movements through the web corpus. Mapping the controversy on the scholarly web. A path leading users through the controversy as it can be traced in the scholalrly literature. Key findings. A short summary of key findings with links to appropriate parts of the map. Procedures for mapping the wild web. A path through the procedures we used to map the wild web. Procedures for mapping the scholarly web. A path through the procedures used to map the scholarly web. References, further reading, and tools. A page offering a list of suggested further reading and descriptions of main tools used in this controversy map.

Procedure for ScienceScape Networks.

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We used an X step process to obtain various network visualizations using a combination of Scopus data, the ScienceScape platform, and the Gephi network analysis software:

1. The underlying Scopus data for scholarly searches were obtained via the procedure described in Step 1 here.

2. The Scopus data were uploaded to "Get Networks" option of ScienceScape.

3. We used the default filtering setting of "Remove disconnected nodes". This procedure removes any data points for attributes such as author, keyword, or source title that appears in the data set but is not connected to any other of those attribute nodes.

4. We allowed the ScienceScape network layout to run and then downloaded the resulting data file for use with Gephi.

5. Using Gephi we ranked node size for given attributes of interest (e.g., keywords, authors, source titles) based on citation numbers so that more highly ranked attribute nodes would appear larger.

6. We ran the "Noverlap" layout to adjust node placement so that no nodes would visually obscure one another (i.e., every node remains visible).

6. We labeled each node using font size proportional to node size (i.e., larger font size corresponds proportionally to node size).