The Post-Human Lyric: Diffractive Vision and the Ethics of Mattering in Adam Dickinson’s Anatomic
Bibliographic record
Abstract
The aim of my inquiry is to discuss Adam Dickinson’s revisionist approach to the lyric autobiography as shown in his most recent volume Anatomic (2018a). Informed by an eco-critical sensibility, the biotechnological gaze, and post-humanist notions of subjectivity, this highly experimental conceptual project reveals porous boundaries of the autobiographical self caught up in the entanglement of the mind and matter. Based on burden tests of the poet’s own bodily fluids, Anatomic offers a philosophical speculation on the nature of the human, asking us to go beyond anthropocentric positioning of the subject and to consider ethical alongside onto-epistemological implications of this new direction. The methodology employed in my analyses of Dickinson’s poems derives from the influential notions of agential realism, diffractive vision, and intra-action formulated by Karan Barad – a trained quantum physicist and feminist philosopher working in the field of science and technology. Barad’s theories fuel New Materialist paradigms of thought as they propose the inherent indeterminacy of matter as well as question the established views of identity and the social. The particular focus of my interrogations will be the relationship between diffractive perception and the medical gaze used by the Canadian conceptualist to see himself non-anthropologically and thus to destabilize the perimeters of the autobiographical self.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".