Frame building

As described in the conceptual framework, for the purpose of policy comparison issue frames are of central importance. Following coding and code standardisation the next step was to construct issue frames for each of the four issues. The frame construction process started from the hypothesis that some of the fields in story grammars are more relevant to the core of the frames then others. The fact that a policy includes information on budgeting is important, but will not be a difference based on which frames substantially differ, so is the case for the qualifier, etc. fields.

The fields that appeared to be most decisive are the norm, actor, location, and causality/mechanism dimension fields. These fields of special importance we named marker fields: these were the fields that ‘mark’ difference between the frames. If documents share certain marker fields, they are likely to share other fields, such as goal, problem, policy action, etc. (to put in a more formalistic model: variance in marker fields explains variance in the other fields). This hypothesis, which proved to be partially true, was also a practical solution for starting the frame construction process: it meant that first we had to concentrate on marker fields and later check if the groups of texts created based on the marker fields are in fact similar to one another in other respects as well.


Hierarchy with number of ocurrences

The first step in using marker fields was to reduce the number of codes for each of the marker fields to a manageable number: occurrence frequencies based on the code hierarchies were used to identify the most relevant values for each of the marker fields in each of the issues. As a next step, co-occurrence of different marker field – value pairs were looked at to see if there are combinations that appear more often than others.

These often occurring combinations served as frame skeletons, which grouped together similar documents based on their marker fields. To give an example: the passive actor/target group of ‘same sex couples’ tends to appear together with the norm of ‘equal treatment’ and the location of ‘economy’ while the ‘LGBT’ or ‘gays and lesbians’ passive actor/target group tend to occur more often with the norm of ‘equality’ in general and causality based on ‘norms’. This observation is reflected in the differentiation between the equal rights for same sex couples and the transformative equality of LGBT people frames.


Table showing co-ocurrence of marker fields

As a last step, the remaining fields (most importantly problem, goal, and policy action) of these groups were looked at to check if they are similar as well. In some cases the groups based on the marker fields had to be divided further based on some other fields to arrive at relatively consistent groups of documents.

The frame list (with frame skeletons and the content of other fields relevant for that group of documents) was sent to country researchers to check if they make sense in their local context, i.e. if the frames identified this way cover the major points in the local debates. In some cases, frames were adjusted (combined, broken up) to arrive at the final list of frames.

As a final step in the data collection/data organisation phase, researchers were asked to recode documents with the help of the frames on the frame list: to decide if a document belongs to a frame or not. This was partly based on the codes recorded for documents (i.e. frame mapping based on correspondence with the marker field values), while in some cases the presence of the frame was identified even though it was not traceable in the marker fields. In these cases, researchers were asked to provide a detailed reasoning of why they think the document matches that frame.