Suggestion Systems for Articles During the Writing Process

The potentiality of suggestion systems is, however, really new. Whereas authors today actively look for literary sources in conventional and digital libraries, innovative technologies enable smart suggestion systems. The insertion of contextbased Internet advertising is a long-established practice, whilst its academic counterpart is still in its infancy. Only Google, in its capacity as trailblazer of search-engine technology, already proposes search-related topics and authors, thus paving the way for the intelligent linking of academics and their publications.

It starts to become exciting when suggestions for potentially interesting, subject-related articles are put forward during the actual writing process. This might to a certain extent release researchers from the somewhat less intellectual task of merely compiling information while simultaneously providing them with additional sources, which they might not have found so easily on their own, since they are only indirectly linked to the topic in question via another association, for instance. Special attention should be paid, when developing the relevant technologies, however, to the selection algorithm, which harbors the risk of tempting the researcher into a convenience trap. The mental blanking out of other sources might represent one aspect of a trap of this kind—a phenomenon that is likewise rooted in the network theory. In this case, the sources that attract most attention are those that are closest to the interests of the researcher in question and are already most visible (cf. Barabási and Albert 1999). The predefined ranking of pop-up results is another hazard. There are countless analyses of the recorded click rate for search results using the Google search engine. Various analysis in Google Analytics reports conducted over several years have repeatedly provided a similar picture—about 80 % of all clicks landed on the first five search results that appeared on the screen, 18 % on the remaining ones on the first page and only 2 % on the second page. This data has been retrieved by comparing search statistics with click statistics. Due to their previous experience and working routines, one can assume that academics conduct their research more thoroughly than general consumers. Nevertheless, such attributes as convenience and circumstances like being in a hurry are only human and also apply to a certain extent to researchers, which bodes quite well for the first secondary sources in the list, at least.

Against this backdrop it emerges what a high priority status the algorithm will have with regard to the presentation of suitable secondary literature. Due to the great resemblance in structure, we assume that this feature will operate along much the same lines as search engines, so it is likely to face similar challenges and problems. We will revert to this topic further down, in the section dealing with the presentation of results.

Once these technical problems have been solved satisfactorily, we can envisage a completely new form of academic writing, along the lines of the example outlined briefly below:

Example of Academic Writing

A researcher has an idea for an article and already possesses some previous knowledge of the subject-matter, which allows him to put his idea into words straight away. So, using a web application designed specifically for academic writing, he begins to type his idea into the space provided. Since he is logged in, the platform is not only able to create direct references to his previous work and topics processed on the platform but can also read his current input and compare it with texts contributed by other scientists. While he is writing, the researcher can now view context-related excerpts on the screen next to his own text, which might be of interest for the passage he is writing. Other, more general articles dealing with the subject concerned, which might be of relevance to this treatise, appear elsewhere. Based on the topics and contributions evaluated on the platform, the researcher in this particular example also receives suggestions as to which other scientists he should contact for the purpose of exchanging information and views.

This case illustrates a scenario of higher transparency on several levels. Besides those relating to texts, the researcher also receives suggestions relating to people who might prove to be an interesting point of contact. This might conceivably be extended to announcements for specialist conferences, other relevant events or items that match the theme.

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