@@ -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.
