Christina Bergmann

Hier beschreibe ich in einem kurzen Interview sowohl meine Arbeit als Recruiterin als auch für offene Wissenschaft und den Rote Faden Transparenz, der sich durch all meine verschiedenen Projekte zieht.

Eine Keynote im Mai 2019, die die Idee der Community-Augmented Meta-Analyses, implementiert unter anderem in MetaLab, beschreibt.

Abstract - Developmental scientists face unique hurdles to reproducibility - in addition to those highlighted for hypothesis-testing experimental research more generally: We work with an (often) uncooperative population which is difficult to test and recruit, which in turn leads to noisy measures and small sample sizes. To make matters more intransparent, much of the raw data (video and audio recordings in particular, but also many questionnaire responses) are sensitive and thus cannot easily be shared. At the same time, and possibly because of those challenges, developmental scientists have (partially) embraced transparency (see e.g. childes.talkbank.org for decades worth of open data, or more recently wordbank.stanford.edu, lookit.mit.edu, and manybabies.stanford.edu). A consequence are unique solutions to our hurdles, which may also be of interest to other experimental researchers. One example among those I will discuss are "walkthrough videos" to provide detailed documentation of the procedure in the absence of sharing actual recordings of experiments. Such videos can be highly valuable, for example for teaching and to uncover systematic methodological variation.

Ein remote Vortrag im April 2022 zum Thema Zusammenarbeit und Open Data.

Vorstellung des ManyBabies Projektes auf einer Konferenz zur wissenschaftlichen Kollaboration, der PSACon 2021.

Eine Kombination aus Open Science und meiner Psycholinguistischen Forschung.

Abstract: Transparency and cumulative thinking are key ingredients for a more robust foundation for experimental studies and theorizing. Empirical sciences have long faced criticism for some of the statistical tools they use and the overall approach to experimentation – a debate that has in the last decade gained momentum in the context of the “replicability crisis.” Many solutions were proposed, from open data, code, and materials – rewarded with badges – over preregistration to a shift away from focusing on p values. There are a host of options to choose from; but how can we pick the right existing and emerging tools and techniques to improve transparency, aggregate evidence, and work together? I will discuss answers fitting my own work on language acquisition spanning empirical (including large-scale), computational, and meta-scientific studies, with a focus on strategies to see each study for what it is: A single brushstroke of a larger picture. My goal is, in all these efforts, to better understand how the lexicon develops across the life span – with an emphasis on early development.

Anlässlich einer Metastudie zum Thema Kindersprache (https://www.nature.com/articles/s41562-022-01452-1) unterhielt ich mich mit Christine Westerhaus darüber, warum wir dazu tendieren, mit Babys über "dudu" und "dada" zu sprechen.