Dear Ada, Thanks a lot for giving me the opportunity to clarify these points, which are very important, and to do so on the public list!
> On 15. Aug 2023, at 13:44, Ada Wan <[email protected]> wrote: > > Dear Gabriella > > I have 2 concerns about your post/project: > > i. I noticed your formulation here in your call "as machine learning > approaches which rely on gold standards which average annotators’ > perspectives are particularly unsuitable for the highly subjective phenomena > tackled in CSS research (e.g., persuasion in online discussions; harmful > communication online; polarization)". I find that a bit unnecessarily > antagonistic towards machine learning (ML). As we know, the driver of textual > processing is data statistics. Statistics (may it refer to data statistics or > statistical methods) is also the science that underlies much of our > computational research in the sciences, including the social sciences. Are > you trying to work on smaller data problems --- nothing wrong with that, btw, > but it could be clearer in the announcement if that's what you are trying to > do? What are you using as gold standard(s) (may it involve ML or not)? Will > you be using ML/statistical approaches/methods, if not, what methods will you > be using for data science? > [Why I am replying to all on this list here:] > I understand that there is sometimes an "anti-ML" and "anti-statistics" > sentiment in the tradition of Computational Linguistics --- it probably > started when grammar/grammarian values were found to not be > portrayable/essential in language data. I just wanted to make sure that this > project does not and would not steer students and practitioners into an > erroneous path of thinking/practice. I absolutely don’t have an anti-ML and anti-statistics sentiment, the contrary! I have been and will continue using ML/statistical approaches and methods. The line of research I have in mind goes in the perspectivist direction ( refer to https://pdai.info/ for an overview), which aims at 1. Developing better data collection/distribution strategies which give credit to annotator perspectives and 2. Developing ML strategies that can help us make better generalizations out of these data. So I hope this makes it clear, that we are all pro-ML and pro-statistics. > > ii. Perhaps I misunderstood, but how should "persuasion in online > discussions; harmful communication online; polarization" be treated as > "highly subjective phenomena" in the context of statistical computing? Where > / in which direction are you trying to go with "high subjectivity"? And what > are the ethical consequences of naming and modeling "highly subjective > phenomena"? I think that acknowledging the high subjectivity of these phenomena (and therefore of the annotations we would use to tackle them in a data-driven approach) gives full credit to the multiple perspectives involved in dealing with them. I think acknowledging this challenge is a very important step in the direction of avoiding ethical consequences. Again, thanks for pointing this out and giving me/us the occasion to think about these points! Best Gabriella > > Thanks in advance for your clarification. > > Best > Ada > > > > > On Tue, Aug 15, 2023 at 9:17 AM Gabriella Lapesa via Corpora > <[email protected] <mailto:[email protected]>> wrote: >> Postdoc and PhD position in NLP/CL/CSS at GESIS (Cologne) >> >> The newly established Data Science Methods team led by Gabriella Lapesa >> [2,3] (Leibnitz Institute for Social Sciences GESIS, Cologne [1], >> Computational Social Science department [4]) has two positions available >> from November 2023: >> - one postdoctoral researcher (100%, 4 years, with possibility of tenure) >> - one doctoral researcher (75%, 4 years). The PhD project will be pursued at >> the Heinrich Heine University of Düsseldorf (where Gabriella Lapesa is a >> junior professor in Responsible Data Science and Machine Learning). >> >> ** The team ** >> >> The Data Science Methods team will contribute to build and mantain the GESIS >> infrastructure for Computational Social Science (CSS) research by developing >> novel methods and making them available, documented, and accessible through >> the GESIS services. The team will focus on fostering the interaction between >> Natural Language Processing and Social Science by developing solutions that >> allow for the integration of multiple information sources (e.g., different >> textual sources for the same debate; socio-demographic features of speakers >> and audiences; integration of textual and multimodal data) and address >> recent challenges in NLP (modeling subjective phenomena; low-resource >> scenarios; identifying and mitigating bias). >> >> The team will tackle research questions at the interface between >> computational argumentation and CSS, and target political communication from >> a very broad perspective involving different types of actors (citizens, >> politicians, parties) and discourse contexts (e.g., online discussions vs. >> newspapers). From a methodological perspective, at the core of the team's >> research agenda will be the “learning from disagreements” challenge, as >> machine learning approaches which rely on gold standards which average >> annotators’ perspectives are particularly unsuitable for the highly >> subjective phenomena tackled in CSS research (e.g., persuasion in online >> discussions; harmful communication online; polarization). >> >> ** How to apply ** >> >> The official job announcement with more details about the requirements/tasks >> and the application procedure can be found at the following links: >> Postdoctoral researcher (deadline: September 5th): >> https://www.hidden-professionals.de//HPv3.Jobs/Gesis//stellenangebot/33073/1 >> Doctoral researcher (deadline: September 6th): >> https://www.hidden-professionals.de//HPv3.Jobs/Gesis//stellenangebot/33084/1 >> >> >> [1] https://www.gesis.org/en/home >> [2] >> https://www.gesis.org/institut/mitarbeitendenverzeichnis/person/Gabriella.Lapesa >> [3] https://www.ims.uni-stuttgart.de/institut/team/Lapesa/ >> [4] >> https://www.gesis.org/en/institute/departments/computational-social-science >> _______________________________________________ >> Corpora mailing list -- [email protected] >> <mailto:[email protected]> >> https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/ >> To unsubscribe send an email to [email protected] >> <mailto:[email protected]>
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