Paweł Szudarski
@pawelszudarski.bsky.social
660 followers
630 following
26 posts
Associate Professor of Applied Linguistics, School of English, University of Nottingham
Expertise: vocabulary, corpus linguistics, TESOL, distance learning, online pedagogy & replication research
https://www.nottingham.ac.uk/english/people/pawel.szudarski
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Tony McEnery
@tonymcenery.bsky.social
· Aug 11
lancasteruniversity: Corpus linguistics and new technologies: data, language and society | edX
In an era of AI and big data, understanding language through data is essential. This hands-on course from Lancaster University—a global leader in corpus linguistics—teaches you to build, query, and an...
www.edx.org
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
Lauren Gawne
@superlinguo.bsky.social
· Aug 5
Position Statement on Generative AI in teaching...
Position Statement on Generative AI in teaching and research
The Linguistics Discipline in the Department of Languages and Cultures at La Trobe University have developed a discipline-specific posi...
www.superlinguo.com
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
JASP
@jaspstats.bsky.social
· Apr 12
"Discovering Statistics Using JASP": A Teaching Guide - JASP - Free and User-Friendly Statistical Software
As the title suggests, the shiny new textbook “Discovering Statistics Using JASP” differs from “Discovering Statistics Using SPSS” mainly in the software that is being used. While the theory sections ...
jasp-stats.org
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
![Preamble
All research at our institution, from ideation and execution to analysis and reporting, is bound by the Netherlands Code of Conduct for Research Integrity. This code specifies five core values that organise and inform research conduct: Honesty, Scrupulousness, Transparency, Independence and Responsibility.
One way to summarise the guidelines in this document is to say they are about taking these core values seriously. When it comes to using Generative AI in or for research, the question is if and how this can be done honestly, scrupulously, transparently, independently, and responsibly.
A key ethical challenge is that most current Generative AI undermines these values by design [3–5; details below]. Input data is legally questionable; output reproduces biases and erases authorship; fine-tuning involves exploitation; access is gated; versioning is opaque; and use taxes the environment.
While most of these issues apply across societal spheres, there is something especially pernicious about text generators in academia, where writing is not merely an output format but a means of thinking, crediting, arguing, and structuring thoughts. Hollowing out these skills carries foundational risks.
A common argument for Generative AI is a promise of higher productivity [5]. Yet productivity does not equal insight, and when kept unchecked it may hinder innovation and creativity [6, 7]. We do not need more papers, faster; we rather need more thoughtful, deep work, also known as slow science [8–10].
For these reasons, the first principle when it comes to Generative AI is to not use it unless you can do so honestly, scrupulously, transparently, independently and responsibly. The ubiquity of tools like ChatGPT is no reason to skimp on standards of research integrity; if anything, it requires more vigilance.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:tfyqeo3tprov5lejatxoi6lb/bafkreidtloh77ocmijnfeddey4fmme43s2mhahdpmr7otrrmvpzjca5kn4@jpeg)
Reposted by Paweł Szudarski
Reposted by Paweł Szudarski
