Reading Forests, Seeing Trees: Visual Poetry with Neurohumanities
Bibliographic record
Abstract
The new contexts of visual materiality engendered by the internet and digital age problematise traditional strategies of critically analysing experimental forms of poetry. By approaching poetry across history as a phenomenon that is both read and seen, this dissertation utilises selected vocabulary, paradigms, and discourses from cognitive neuroscience and psychology to suggest a novel way of analysing contemporary poetry. Under the rubric of Neurohumanities, this project evinces how empirical evidence can be brought into conversation with a literary critical analysis of visual poetry, as a trajectory is mapped from the once-contested combination of art and science to this project?s unique investigation. Within this dissertation, visual poetry and visual poetics are considered in a variety of contexts ? historical, material, critical ? while the case is made for their suitability for empirical analysis. Usurping the standard formula of interdisciplinary approaches to the arts and sciences, in which science is often delegated to `prove? what is already inherently known within a discipline such as poetry studies, the approach demonstrated here suggests how the two disciplines can inform and influence one another. Two Case Studies are presented: one on Canadian visual poetry that focuses on selected works by bpNichol, Derek Beaulieu, and Steve McCaffery; and the other on experimental visual poetry by women, with a focus on selected works by Mirella Bentivoglio, Judith Copithorne, and Susan Howe. The results of a mixed methods psychological experiment with poetry as its subject, conducted for this project, are discussed in tandem with a literary critical close reading. This analysis seeks to account for each Case Study?s respective theoretical engagements with the materiality of language and letterforms, authorial subjectivity, and the gendered experience of voice and silence, combining them with quantitative and qualitative empirical data. This approach is subsequently reflected on from a metacritical perspective, and studied for how this Neurohumanities approach can illuminate the benefits and limitations of combining disciplines. In its focus on small-press publications and historically marginalised poetry, the project engages not just with the interdisciplinary landscape of poetry and science, but with elements of poetry studies that have remained hitherto underappreciated in mainstream literary criticism. Ultimately, in its repudiation of linearity, its questioning of shape and visual perception, and examination of meaning, the dissertation investigates new methods through which two disparate fields might share a commonality both within the scope of this project, and in future forays into the interstices between art and science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".