"Newstead and I stand or fall together": Memorial Ecology and Multispecies Agency in Byron's Early Poetry
Bibliographic record
Abstract
Scholars studying memory, literary tourism, and Byron all note the cooperation between author and audience at work in memorials--be it in terms of speech and response, hospitality and reception, or memory and forgetting. None, however, address the environment at Newstead as an agentic being involved with Byron's memorial legacy. Byron acknowledged multispecies beings as important actors in his eventual legacy. Through some of his early poems, we see the land under and around Newstead Abbey, as well as its nonhuman life, exercising agency and affecting Byron's memory. I limit my analysis to Byron's early poetry partly to trace how a younger, more earnest Byron relied on Romantic memory-building culture and partly to focus on the effects that Newstead had on Byron's legacy. My primary objects of study are the following poems: "On Leaving Newstead Abbey" (composed 1803), "To an Oak in the Garden of Newstead Abbey" (1807), "Elegy on Newstead Abbey" (1807), and "Inscription on the Monument of a Newfoundland Dog" (1808). Each of these addresses the Byrons' ancestral estate as an ecology which Byron imbued with poetic purpose, and the core location of his youthful legacy-building project. I address the poems in chronological order to show how Byron recruits and unites different voices to support his legacy. Focusing on Newstead in this sense sheds light on any number of related phenomena pertaining to Byronism, especially monuments, Byron's home, and other aspects of material culture that honored Byron's posthumous legacy. Given that, within years of writing these four poems, Byron would become known worldwide as the quintessential Romantic poet, his ancestral home, like other things and spaces that came to stand in for him, offers a highly useful and arguably paradigmatic case study. That it is not just a monument, but a composite being acting in and made up of literal and memorial ecosystems, suggests a kind of memorial agency or voice emerging from Newstead. This influence supports what Byron poetically speaks about and into Newstead and expands our notion of what effective memorials entail, effectively advocating for more and better study of environmental actors within reception studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".