Explicating Objectual Understanding: Taking Degrees Seriously

Baumberger, Christoph

doi:10.1007/s10838-019-09474-6

Cited by 38 publications

(38 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…What is more, it is generally acknowledged in the philosophical literature, if only implicitly, that understanding comes in degrees (although this may not have been taken seriously enough, see Baumberger 2019). One of the main points of the present paper is that there is a continuum in climate modelling with respect to various criteria of understanding.…”

Section: Introductionmentioning

confidence: 81%

Understanding climate change with statistical downscaling and machine learning

2020

View full text Add to dashboard Cite

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based 'black boxes' that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. * We thank the participants of the philosophy of science research colloquium in the Spring semester 2020 at the University of Bern for valuable feedback on an earlier draft of the paper. We also wish to thank the participants of the seminar 'Philosophy of science perspectives on the climate challenge' and the workshop 'Big data, machine learning, climate modelling & understanding' in the Fall semester 2019 at the University of Bern and supported by the Oeschger Centre for Climate Change Research. JJ and VL are grateful to the Swiss National Science Foundation for financial support (grant PP00P1_170460). TR was funded by the cogito foundation.

show abstract

Section: Introductionmentioning

confidence: 81%

Understanding climate change with statistical downscaling and machine learning

2020

View full text Add to dashboard Cite

show abstract

“…to learn the outcome for a specific initial condition. 16 One further preliminary note before I specify my explication: Understanding, which figures centrally in my explication, comes in degrees (see Baumberger, 2019). This is presupposed by ordinary talk, for instance, when we speak of better or deeper understanding.…”

Section: A New Explication Of Opacitymentioning

confidence: 99%

Opacity thought through: on the intransparency of computer simulations

Beisbart

2021

Synthese

View full text Add to dashboard Cite

Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus cannot be checked by hand, this doesn’t exhaust what we might want to call the opacity of simulations. I thus make a fresh start with the natural idea that the opacity of a method is its disposition to resist knowledge and understanding. I draw on recent work on understanding and elaborate the idea by a systematic investigation into what type of knowledge and what type of understanding are required if opacity is to be avoided and why the required sort of understanding, in particular, is difficult to achieve. My proposal is that a method is opaque to the degree that it’s difficult for humans to know and to understand why its outcomes arise. This proposal allows for a comparison between different methods regarding opacity. It further refers to a kind of epistemic access that is important in scientific work with simulations.

show abstract

“…It can be inferred from the description of notions such as powerful knowledge that knowledge must be interpreted broadly, and that it must not be understood as merely having facts at the heart of a curriculum. Rather, knowledge in such discussions relates to understanding how the world works (Baumberger, 2019). As Baumberger et al (2016, p. 1) point out, understanding is 'a central good that we try to realize when we think about the world' , which surpasses factual knowledge as such (Grimm, 2012).…”

mentioning

confidence: 99%

The conceptual importance of grammar

Rijt

Coppen²

2021

View full text Add to dashboard Cite

Knowledge about Language (KaL) is an important part of L1 language education around the world. A controversial part of KaL is grammatical or syntactic knowledge, i.e., knowledge about the form, meaning and use of sentences and phrases. In the current international discourse on L1 grammar teaching, grammar is principally motivated by the desire to enhance students’ literacy development, befitting the communicative turn in mother tongue education and following high quality research which has shown that contextualized grammar teaching can impact on students’ writing development. However, there are also other potentially meaningful reasons to teach grammar, which remain underresearched and underdiscussed in curriculum discussions: (1) the general importance of language justifies that L1 speakers understand how their language works; (2) grammar teaching provides more insight into the workings of the human mind; (3) grammar teaching can be used to facilitate students’ reasoning and stimulate their critical thinking abilities. These reasons for teaching grammar do not necessarily relate to literacy development; rather, they pertain to a general conceptual importance of knowledge about grammar. This paper explores these arguments and argues, partly based on empirical evidence from recent research, that knowledge-related rationales deserve a more prominent place in curriculum discussions about grammar teaching.

show abstract

Explicating Objectual Understanding: Taking Degrees Seriously

Cited by 38 publications

References 47 publications

Understanding climate change with statistical downscaling and machine learning

Understanding climate change with statistical downscaling and machine learning

Opacity thought through: on the intransparency of computer simulations

The conceptual importance of grammar

Contact Info

Product

Resources

About