diff --git a/thesis/5-Overview-EN-Wiki.tex b/thesis/5-Overview-EN-Wiki.tex
index 8a58b9f2724cb6dfa957cdea05d01477e2dee36b..d07946808be809e9649cbed00431eeeebca11afa 100644
--- a/thesis/5-Overview-EN-Wiki.tex
+++ b/thesis/5-Overview-EN-Wiki.tex
@@ -37,13 +37,13 @@ For further reference, the schemas of all four tables can be viewed in figures~\
 \section{Types of edit filters: Manual Classification}
 \label{sec:manual-classification}
 
-In order to get a better understanding of what exactly it is that edit filters are filtering, I applied a grounded theory inspired emergent coding(see chapter~\ref{chap:methods}) to all filters, scrutinising their patterns, comments and actions.
+In order to get a better understanding of what exactly it is that edit filters are filtering, I applied emergent coding (see section~\ref{sec:gt}) to all filters, scrutinising their patterns, comments and actions.
 Three big clusters of codes were identified, namely ``vandalism'', ``good faith'', and ``maintenance'', as well as the auxiliary cluster ``unknown''.
 These are discussed in more detail later in this section, but first the coding itself is presented.
 
 \subsection{Coding process and challenges}
 
-As already mentioned, I started coding strongly influenced by the coding methodologies applied by grounded theory scholars (see section~\ref{sec:gt}) and let the labels emerge during the process.
+As already mentioned, I applied emergent coding and let the labels originate directly from the data.
 I looked through the data paying special attention to the name of the filters (``af\_public\_comments'' field of the \emph{abuse\_filter} table), the comments (``af\_comments''), the pattern constituting the filter (``af\_pattern''), and the designated filter actions (``af\_actions'').
 
 The assigned codes emerged from the data: some of them being literal quotes of terms used in the decription or comments of a filter, while others summarised the perceived filter functionality.