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Before drawing generalisations from the studies in our systematic review, however, we need to consider several features.
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Furthermore, one may be critical about the complexity and fluidity in the notions of superdiversity and bricolage which seem to interfere with scientific goal of drawing generalisation from local findings (and, thereby, reduce complexity).
By the same token, the heterogeneous nature and diverse identity of emerging donors makes it difficult to draw generalisations based on only few cases.
A larger number of neighbourhoods and countries would be desirable in order to draw generalisations regarding welfare state regimes.
Recognising how this diversity shapes the experiences of support needs in individual patients is important in order to avoid drawing broad generalisations.
These approaches can learn implicit patterns in annotated data, allowing them to draw better generalisations than manually constructed rules.
A second limitation is the small sample size of physicians involved in this study and therefore it is difficult to draw broad generalisations from the study.
Additionally, the final sample of thirty social workers is a relatively small sample from which to draw broad generalisations regarding the capacity of social workers.
As a result it is not possible to draw wide generalisations from this study, although the breadth and depth of the perceptions obtained may aid in the transferability of findings.
For example, a researcher concerned with drawing a statistical generalisation across an entire population may administer a survey questionnaire to a representative sample population.
It is difficult to make generalisations.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com