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Mapping Diversity: Developing a European Classification of ... - U-Map

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While rankings are criticized for their conceptual and methodological problems and for their potentially<br />

10<br />

dysfunctional effects, they are nevertheless seen as ‘here to stay’. The challenge therefore is to <strong>of</strong>fer<br />

constructive contributions to the process <strong>of</strong> improving the quality and effectiveness <strong>of</strong> rankings. This<br />

is one <strong>of</strong> the intentions <strong>of</strong> this project.<br />

2.1.3 <strong>Classification</strong>s<br />

‘A classifi cation is a spatial, temporal or spatio-temporal segmentation <strong>of</strong> the world’ (Bowker and<br />

Star 2000, p.10). Or in simpler terms it is ‘… the general process <strong>of</strong> grouping entities by similarity’<br />

(Bailey 1994, p.4).<br />

In the literature on classifi cations, a number <strong>of</strong> related terms are used, sometimes interchangeably,<br />

which can lead to confusion. In order to be explicit about the concepts used in this project we<br />

provide a short resumé <strong>of</strong> the relevant terms.<br />

A classifi cation should be distinguished from a typology. A typology is a conceptual classifi cation.<br />

A classifi cation orders empirical cases while a typology addresses conceptual entities. The cells in<br />

a typology represent concepts rather than empirical cases. These concepts are generally defi ned<br />

in a monothetic way: they comprise entities that are all identical on all variables or dimensions<br />

measured.<br />

A taxonomy is a special case <strong>of</strong> classifi cation with the main difference being that each cell (taxon)<br />

comprises an empirical case. This term is generally used in biological sciences.<br />

In this project we are building a classifi cation: we develop a set <strong>of</strong> grouping criteria and use it to group<br />

empirical cases (in our case: higher education institutions). Classifi cations can be unidimensional<br />

or multi-dimensional. In this project a multi-dimensional classifi cation is aimed for.<br />

Generally speaking, classifi cations help to describe a fi eld. They may contribute to the reduction <strong>of</strong><br />

complexity and to increasing transparency. In addition they may be used to identify similarities and<br />

differences between entities. A classifi cation is also an instrument for information and communication.<br />

It intends to assist stakeholders in their decisions and actions.<br />

MAPPING DIVERSITY<br />

As is the case with rankings, in classifi cations the selection <strong>of</strong> the entities to be classifi ed and<br />

particularly <strong>of</strong> the ‘grouping criteria’ to categorize these entities are crucial decisions. Building a<br />

classifi cation should therefore be a user-oriented process. The most crucial aspect <strong>of</strong> a classifi cation<br />

is to determine who the potential or intended users (stakeholders) are and what they want to use<br />

the classifi cation for.<br />

Classifying entities is a process that consists <strong>of</strong> a number <strong>of</strong> steps. The fi rst one is to identify the<br />

entities to be classifi ed. The user-oriented perspective provides suffi cient guidance here.<br />

Once the entities for the classifi cation have been identifi ed the second step can be taken: the<br />

defi nition <strong>of</strong> relevant and adequate grouping criteria. The choice <strong>of</strong> the dimensions (as we shall call<br />

the grouping criteria or key characteristics from now on) should allow the users <strong>of</strong> the classifi cation<br />

to group the entities the way they want. The more dimensions are selected the more the entities can<br />

be grouped and described in detailed and different ways. Here again, the user-oriented perspective<br />

is crucial. Only when the relevant stakeholders are able to contribute to the selection and defi nition<br />

<strong>of</strong> dimensions can relevant classifi cations be produced.<br />

The fi nal step is to identify how the entities score on the different dimensions. During this step the<br />

entities are allocated to the cells <strong>of</strong> the classifi cation on the basis <strong>of</strong> empirical information.

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