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Open AccessJournal ArticleDOI

Conceptions of scientific progress in scientific practice: an empirical study

Moti Mizrahi
- 01 Dec 2021 - 
- Vol. 199, Iss: 1, pp 1-20
TLDR
In this article, the authors employ the methods of data science and corpus linguistics to compare the epistemic and noetic accounts of scientific progress in philosophy of science, and conclude that the noetic account is superior to the semantic account.
Abstract
The aim of this paper is to contribute to the debate over the nature of scientific progress in philosophy of science by taking a quantitative, corpus-based approach. By employing the methods of data science and corpus linguistics, the following philosophical accounts of scientific progress are tested empirically: the semantic account of scientific progress (i.e., scientific progress in terms of truth), the epistemic account of scientific progress (i.e., scientific progress in terms of knowledge), and the noetic account of scientific progress (i.e., scientific progress in terms of understanding). Overall, the results of this quantitative, corpus-based study lend some empirical support to the epistemic and the noetic accounts over the semantic account of scientific progress, for they suggest that practicing scientists use the terms ‘knowledge’ and ‘understanding’ significantly more often than the term ‘truth’ when they talk about the aims or goals of scientific research in their published works. But the results do not favor the epistemic account over the noetic account, or vice versa, for they reveal no significant differences between the frequency with which practicing scientists use the terms ‘knowledge’ and ‘understanding’ when they talk about the aims or goals of scientific research in their published works.

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Citations
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Journal ArticleDOI

Modeling and corpus methods in experimental philosophy

TL;DR: This article reviewed the quasi-experimental studies that have been done using textual data from corpora in philosophy, with an eye for the modeling and experimental design that enable statistical inference, and found that most studies forego comparisons that could control for confounds, and only a little less than half employ statistical testing methods to control for chance results.
Journal ArticleDOI

What Is the Basic Unit of Scientific Progress? A Quantitative, Corpus-Based Study

TL;DR: In this paper , the same methods of text mining and corpus analysis used by Mizrahi (2021) were used to test empirically a philosophical account of scientific progress, namely, the so-called functional-internalist account according to which the aim or goal or scientific research is to solve problems.
References
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Book

Knowledge and Its Limits

TL;DR: In this article, a state of mind is defined as broadness, broadness of the mind, prominentness, anti-lightness, and anti-reflectivity of a person.
Book

Progress and its problems towards a theory of scientific growth

TL;DR: In this paper, Laudan constructs a remedy from historical examples that involves nothing less than the redefinition of scientific rationality and progress, and expands the notion of "paradigm" to a research tradition, thus providing a meta-empirical basis for the commensurability of competing theories.
Journal ArticleDOI

History of Science and Its Rational Reconstructions

TL;DR: In this article, it is argued that history of science without history of philosophy of science is empty, and history without philosophy-of-science is blind, and that any rational reconstruction of history needs to be supplemented by an empirical (socio-psychological) external history.
Journal ArticleDOI

Plant metabolomics: From holistic hope, to hype, to hot topic

TL;DR: By creating unique opportunities for us to interrogate plant systems and characterize their biochemical composition, metabolomics will greatly assist in identifying and defining much of the still unexploited biodiversity available today.
Journal ArticleDOI

SLOPE { Adaptive Variable Selection via Convex Optimization

TL;DR: SLOPE as mentioned in this paper is the solution to the sorted L-one penalized estimator, where the regularizer is a sorted l1 norm, which penalizes the regression coefficients according to their rank: the higher the rank, stronger the signal, the larger the penalty.
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