‘Explorational Blankness’: Twentieth- and twenty-first-century poets rewrite astronomy’s hidden and expanding universe
Bibliographic record
Abstract
The intersections between modern poetry and modern astronomy remain largely unmapped. This thesis seeks, in part, to correct this situation by exploring a range of poetry that engages with astronomy or cosmology. In a first part, this study analyses a number of anthologies of astronomical poems. The result of this analysis suggests not only that poets engaging with the universe and astronomy write in many different forms and genres, but also that references to spaceflight and the universe are used to gain a wider perspective on terrestrial affairs and give rise to often impassioned poems about political and social injustices and metaphysical concerns with the comparative insignificance of human existence in the face of cosmic expansion and expansiveness. The thesis proceeds, in separate chapters, with analyses of the works of five very diverse and partly under-studied poets: American poets Tracy K. Smith, Will Alexander, Amy Catanzano, Scottish poet Edwin Morgan, and Canadian poet-astronomer Rebecca Elson. Through a series of close readings of selected poems by these writers this thesis argues that these astronomical poems create starkly diverging images of the cosmos: the poems demonstrate that the universe often serves as an abstract creative space for various political agendas, social activism, literary and formal innovation, and, indeed, for astronomical research as well.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".