@article{SalaKabeshkin2022,
  author    = {Sala, Lorenzo and Kabeshkin, Anton},
  title     = {A priori philosophy of nature in Hegel and German rationalism},
  series = {British journal for the history of philosophy : Bjhp},
  volume    = {30},
  journal   = {British journal for the history of philosophy : Bjhp},
  number    = {5},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {London},
  issn      = {0960-8788},
  doi       = {10.1080/09608788.2022.2044753},
  pages     = {797 -- 817},
  year      = {2022},
  abstract  = {Hegel's many remarks that seem to imply that philosophy should proceed completely a priori pose a problem for his philosophy of nature since, on this reading, Hegel offers an a priori derivation of empirical results of natural sciences. We show how this perception can be mitigated by interpreting Hegel's remarks as broadly in line with the pre-Kantian rationalist notion of a priori and offer reasons for doing so. We show that, rather than being a peculiarity of Hegel's philosophy, the practice of demonstrating a priori the results of empirical sciences was widespread in the pre-Kantian rationalist tradition. We argue that this practice was intelligible in light of the notion of a priori that was still quite prominent during Hegel's life. This notion of a priori differs from Kant's in that, while the latter's notion concerns propositions, the former concerned only their demonstration. According to it, the same proposition could be demonstrated both a posteriori and a priori. Post-Kantian idealists likewise developed projects of demonstrating specific scientific contents a priori. We then make our discussion more concrete by examining a particular case of an a priori derivation of a natural law, namely the law of fall, by both Leibniz and Hegel.},
  language  = {en}
}
@article{SchadVasishthHohensteinetal.2020,
  author    = {Schad, Daniel and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold},
  title     = {How to capitalize on a priori contrasts in linear (mixed) models},
  series = {Journal of memory and language},
  volume    = {110},
  journal   = {Journal of memory and language},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2019.104038},
  pages     = {40},
  year      = {2020},
  abstract  = {Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-tests. Reproducible code is available from https://osf.io/7ukf6/.},
  language  = {en}
}