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🧲 Magnetic labels
You have already coded your dataset, manually or using AI, and now you want to relabel.
Suppose you already know what labels you want to use, perhaps:
- you knew before you even started
- you decided what labels you wanted after reviewing your data and looking at different auto-cluster solutions
Magnetic labels are a really simple solution for these cases.
You simply type the list of magnetic labels you want and decide on the power of the magnets.
Magnetic labels attract existing labels of similar meaning, essentially relabelling these old labels with the new magnetic label. If an existing label is similar to two or more different labels, it is relabelled with the magnetic label it is most similar to.
If you use low magnetism, the magnets are weak and only attract existing labels which are very similar to them.
Increasing the magnetism means that more and more existing labels are attracted to the magnetic labels.
Existing labels which are not attracted to any label are unchanged. This means that you can easily see if your magnetic labels cover most of the original content.
Best practice is then, after applying magnetic labels, to then auto-cluster the links in order to pick out important themes which are not covered by the magnetic labels.
Use cases
Drop in magnetic labels which contain the text from the “official” theory of change.
- See how much the existing labels get attracted to the magnetic labels, and what material is left over.
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