SHELLEY, BLAKE, AND COLERIDGE WALK INTO A CHURCH: ALTERNATIVE VISIONS OF DIVINITY IN THE ROMANTIC SUBLIME
Bibliographic record
Abstract
Preface and Acknowledgements There is a particular sensation that occurs in my brain when I study Romantic literature. It’s not the only thing that induces the sensation, but it is one of the most reliable. I was thirteen years old the first time I read Samuel Taylor Coleridge’s “Kubla Khan” and felt like someone had poured a carbonated, caffeinated soda directly into my mind. It is a sensation of vitality and presence, and awake-ness that both grounds me and encourages me to fly. So I suppose I must begin my acknowledgements with gratitude for Lyrical Ballads, for the engineers of the British Romantic Movement, and for the poetry of Percy Bysshe Shelley, William Blake, and Samuel Taylor Coleridge for making my brain effervescent and shaping my worldview, spirituality, and scholarship. I would additionally like to thank my friends and family for listening to my endless screaming, whining, obsessing, and enthusing, and for their endless patience and support of my work and wellbeing. Writing this thesis during COVID-19 has been one of the most challenging experiences of my life, but thanks to my loved ones’ encouragement and solidarity, it has also been among the most rewarding. Finally, thank you to Drs. Bruhn and McGill, my amazing advisor and reader, for their invaluable insight, guidance, and expertise, for their poignant questions and thoughtful engagement, and for their patience for sleep-deprived emails at six o’clock in the morning. My deepest gratitude and regards to everyone who has been with me on this journey. Beannachtaí oraibh.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".