Network Analysis in Literature and the Arts: Rethinking Agency and Creativity
Bibliographic record
Abstract
Abstract The essay proposes to think of the creative subject as an actor in a network, that is, following Bruno Latour, as a »moving target of a vast array of entities swarming toward it« (Latour 2005, 46). It explores what it means to bring a network analysis to lectures on poetics by employing both a structuralist visualization informed by a computational method and a sociological method according to Latour’s Actor-Network-Theory (ANT). ANT is used to make traceable what with Hugh Kenner is called »elsewhere communities« consisting of spirits and minds along with objects and spaces. This serves to defend a method of criticism that is not oriented towards unearthing deep textual meanings, but which foregrounds the arts’ relatability and potential for provoking association and attachments. Network analysis in the arts and humanities, so goes the argument, has the potential to be much more than a formalist description of connections made. It offers means for detecting the implicit and explicit presences of a variety of different actors in or relating to works of art and challenges us to move beyond established analytical categories such as intertextuality and intermediality by opening the inquiry to a wider diversity of actors and to redefine our understanding of creativity. The article focuses on networks that emerge in lectures in which renowned artists from around the world share with general audiences their views on work processes, motivations to create, and artistic self-understandings. These are known asPoetikvorlesungenin Germany, Austria, and Switzerland, but do not have a distinct label outside of the German-speaking literary scene. The article departs from the observation that making connections and forming artistic associations are key components of these lectures as this feature can be found frequently. It first outlines genre characteristics of lectures on the arts with particular focus on networks that such lectures participate in. Emblematic examples are the Frankfurt Lectures on Poetics by the German novelist Daniel Kehlmann (given in 2014) and the Tanner Lectures by the Canadian writer and critic Hugh Kenner (given in 1999). Kehlmann depicts his artistic influences, sources of inspiration, and references to existing contexts by pretending to summon spirits, a rhetorical gesture akin to a necromancy. Kenner calls networks that evolve from making such connections »elsewhere communities«. The essay explores what a network-oriented analysis of this genre could look like by turning to the Norton Lectures by the German filmmaker Wim Wenders (given in 2018). These serve to test two different analytical approaches. The article relies both on network visualization and on tracing of networks according to Actor-Network-Theory (ANT). The article thus offers both a graphic representation of references from Wenders’ lectures and a textual tracing of associations according to the methodology outlined by Latour. An important finding is that network analysis, no matter its method, offers exciting opportunities in revealing the significance of relations and associations. Network analysis challenges literary scholars to revisit and rethink intertextuality, intermediality, or intersubjectivity and invites them to unearth a new diversity of actors. The article argues that graphic visualizations done with computational methods can be instrumental in the immediacy with which they communicate findings, especially when it comes to findings from a large corpus. The article then moves on to explore the nuances and theoretical implications that ANT offers in addition to the network visualization. A major appeal of ANT is, so the argument, that it offers insight into processes that are central to literary criticism: translation, mediation, and the evolving dynamics that stem from them. Since lectures on poetics are located at the intersection of artistic creation, authorial self-presentation, and criticism, they offer a particularly good window into the interactions ofpoiesisandaesthesis, of creative work and its dependency on the reception of other artworks. The argument concludes by suggesting that network analysis invites theorists to reconceptualize reader-response theory toward what scholars call comparative media studies. Finally, the article briefly considers lectures on the arts not as objects of criticism, but as a blueprint for scholarship that looks to re-envision literary criticism and its engagement of the reading public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".