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Record W7111837588

The Post-Human Lyric: Diffractive Vision and the Ethics of Mattering in Adam Dickinson’s Anatomic

2020· other· en· W7111837588 on OpenAlexaboutno aff

Bibliographic record

VenuePressto (Uniwersytetu Adama Mickiewicza) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMaterialismSubject (documents)AnthropocentrismGazeFocus (optics)Field (mathematics)Indeterminacy (philosophy)PerceptionIdentity (music)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.296
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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