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“What is Dead May Not Die”: Locating Marginalized Concepts Among Ordinary Biologists


Historians and biologists identify the debate between mechanists and vitalists over the nature of life itself with the arguments of Driesch, Loeb, and other prominent voices. But what if the conversation was broader and the consequences deeper for the field? Following the suspicions of Joseph Needham in the 1930s and Francis Crick in the 1960s, we deployed tools of the digital humanities to an old problem in the history of biology. We analyzed over 31,000 peer-reviewed scientific papers and learned that bioexceptionalism participated in a robust discursive landscape throughout subfields of the life sciences, occupied even by otherwise unknown biologists.

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Fig. 1


  1. To be sure, Lloyd Morgan denied such a categorization: “I suppose that it is because you think it out to be called vitalism that you class me among the neo-vitalists. I’m afraid they won’t own me…” (C. Lloyd Morgan to Joseph Needham; quoted in Peterson 2016, p. 52).

  2. Ideas come later, “in every sense,” to the laboratory practice of biology (Endersby 2009, p. 26).

  3. Jacques Loeb to Raymond Pearl, 21 Jan. 1913, Jacques Loeb Papers, Library of Congress, Washington, DC, USA.

  4. To be clear, we conducted a variety of keyword searches, but found little material of value.

  5. A notable exception to this is Roe (1961).

  6. This process occurred in the Lysenko context as well, according to Strządała (2020).

  7. This tool can be found at

  8. In this way, we hope to have followed at least one of Lauren Klein’s recommendations (2018).

  9. Peirson et al. (2017) provided an extremely technical explanation of this process in the main body of their study of JHB. For a more detailed discussion of the appropriate model calibration we use, see Appendix.

  10. Copyright issues precluded including texts after 1970 at the large scale we were after, though we did examine individual articles after this date in the traditional close-reading manner.

  11. JSTOR would not release issues of Science, despite having granted Blei and Lafferty (2007) access to the journal contents for their project.

  12. For a recent example of the application of these methods, see Wehrheim (2019).

  13. As discussed in Appendix, we removed stop words, or closed-class words, after identifying the passages, which reduced some of the shortest passages to 10 words.

  14. Note that Commoner never uses the word vitalism. Nonetheless, his open resistance to what he would label reductionist or mechanistic views gets him labeled as such by opponents such as Crick.

  15. The brackets include the portion of the sentence that was not in the topic model in order to provide still greater context.

  16. The topic was driven by three articles in particular: Lepeschkin (1931), Brillouin (1949), and Weiss (1970). The JSTOR article IDs for the other 12 articles are 2456128 (1912), 2808239 (1927), 2808186 (1928), 22808375 (1938), 457319 (1940), 4604480 (1940), 87517 (1942), 87518 (1942), 4604975 (1946), 4605422 (1952), 27826597 (1955), and 278383338 (1962).

  17. This framing belongs to Scott (1985). Curiously, the notion that the weak can have weapons within a closed, quasi-hegemonic circle dates back to sociological studies of the 1930s first applied to the professoriate itself (Warren 1941). Such weapons in the 1930s included: grade-inflation to limit student complaints, pomposity to compensate for a lack of teaching skill in one’s courses, and glad-handing prominent scholars at conferences and meetings to raise one’s stock by having been observed associating with the “right people.”


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Thank you to Rachel Foshag, Scott Pontasch, and the ITHAKA staff, Alex Boucher at University of Alabama Libraries, several anonymous reviewers, and the very patient editors.

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Appendix: How We Established the Number of Topics in Each Model

Appendix: How We Established the Number of Topics in Each Model

We found sparse documentation on which method to use under what circumstances, and we wanted to embrace the hermeneutica ethos of iterative understanding (Rockwell and Sinclair 2015). This led us to focus on three methods:

  1. 1.

    Scholarly interpretation of top words output for multiple models of the same data set using LDA (Blei et al. 2003) and Collapsed Gibbs Sampling as provided by the lda R package (Chang 2015). We limited our use of this method for the smallest models.

  2. 2.

    Topic tuning using the ldatuning package in R, specifically the function FindTopicNumber using three metrics named for the associated publications (Griffiths 2004; Cao et al. 2009; Arun et al. 2010) with the Collapsed Gibbs Sampler method for creating the model (Nikita 2017).

  3. 3.

    Five-Fold Cross Validation using LDA and the Collapsed Gibbs Sampler method, also part of the ldatuning R package.

Each method suggested slightly different topic numbers, and the ldatuning tools provide a range, not a specific value. As a result, we built nearly 200 models to explore these variations. Other than K (the number of topics), the parameters were consistent: alpha 0.2, beta 0.2, burn-in iterations 5000. Stop words were compiled from the standard English list used by Voyant Tools, with the addition of Roman and Arabic numerals, special characters, and terms related to the genre of scientific writing: appendix, conclusion, discussion, figure, fig, graph, introduction, methods, references, results, table, volume, vol. Voyant’s list of stop words included common adverbs, auxiliary verb forms, comparatives, conjunctions, prepositions, and pronouns. When building the models, we used the set.seed() function in R for reproducibility, using the value of 357 in a demonstration of the LDAvis package (Sievert 2014). Note, we did not use LDAvis, but repurposed topic modeling code from a different project. While this exploratory method did point us to articles that answered our questions, and we document the details for reproducibility, in future projects we would revise this method for efficiency.

For the first method–scholarly interpretation–we relied on attributes inspired by Chang et al. (2009) as ways of quantifying human interpretability of topics: word intrusion and topic intrusion. We approached our reading of the top high-probability terms in the topics as though any word or topic could be an intrusion. We read the output of the modeling algorithms with this skepticism, balanced by knowledge of the field and period. That is, our exploration of the models focused on two aspects: semantic consistencies in top words in topics across models and the ability of the topics to identify relevant passages, as we describe in the main body of text.

As indicated, the second method–the tuning process–deployed three metrics for evaluation of perplexity, which in topic modeling is a measure of how well the probabilistic model describes an unseen sample. Each method produced different quantitative results, which pushed us to assess models built with the topic numbers suggested by the extrema of minimum perplexity and the ranges in which perplexity was consistent across other methods.

Five-Fold Cross Validation is a method that uses 80% of the corpus to build the model and then tests the model against the held-out passages. This occurs five times for each possible number of topics, so that passages are alternatingly part of the training corpus or part of the test data. The algorithm returns perplexity scores for each of the five models. Using a polynomial regression, we determined the topic number with the lowest overall perplexity, often a range.

Below is a summary of the output from the methods to establish the number of topics for the keywords presented in this article (Table 9).

Table 9 Suggested number of topics for models built on different size context around the four keywords presented in this paper using the methods described above

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Peterson, E.L., Hall, C. “What is Dead May Not Die”: Locating Marginalized Concepts Among Ordinary Biologists. J Hist Biol 55, 219–251 (2022).

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  • Mechanism
  • Vitalism
  • Digital humanities
  • Computational textual analysis
  • Twentieth century
  • Joseph Needham
  • Francis crick