Examples taken from Duranteye (2020):
https://pdf-tei-editor.panya.de/#pdf=e7z9b6&xml=nf62nu
Currently, there is no adequate label for references to laws like this:
Labels explicitly identifiying the titel ('UrhG') as a law or norm and a differentiation between section ('§') and subsection ('Abs. 2') should suffice for capturing the information.
Court rulings, on the other hand, are often embedded in a complex reference structure leading to automatic mislablings like this:
Generally, the segmentation could be done in terms of currently available labels, e.g. like this:
However, at least three new issues arise with this segmentation:
- 'Tz.' (= marginal number) is not actually equal to page numbers and can be especially misleading when actual page numbers either not exist at all or are derived from the version of the ruling published in a journal ( in this case 'GRUR'). Should there be a differentiation between page and marginal numbers?
- 'GS Media/Sanoma' is a header used as shorthand for finding the case in question. It is not essential for the core information of the reference, but it inherently belongs to it (as opposed to elements usually labeled as notes). In any case, it has to stop being identified by the model as starting a new .
- The court is not really an author and the case number not really a title. Is Orgname more adequate? Should the actual case id have its own label?
@lfoppiano @cboulanger
Examples taken from Duranteye (2020):
https://pdf-tei-editor.panya.de/#pdf=e7z9b6&xml=nf62nu
Currently, there is no adequate label for references to laws like this:
Labels explicitly identifiying the titel ('UrhG') as a law or norm and a differentiation between section ('§') and subsection ('Abs. 2') should suffice for capturing the information.
Court rulings, on the other hand, are often embedded in a complex reference structure leading to automatic mislablings like this:
Generally, the segmentation could be done in terms of currently available labels, e.g. like this:
However, at least three new issues arise with this segmentation:
@lfoppiano @cboulanger