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.