'O that I could a sin once see!' : Sin and Scrupulosity in George Herbert's The Temple
Bibliographic record
Abstract
In The Temple, George Herbert uses a host of surprising and challenging images for sin.In "Sin (2)," he appears to subscribe to the theological notion of evil as privation; yet in many of his poems, he gives sin a material presence in a way that encourages fixation.This paper links Herbert's representations of sin to scrupulosity.It argues that an unease towards the immaterial prompts several of his speakers to depict their wrongdoings in ways that allay their scrupulous inclinations to make spiritual realities visible and measurable.Specifically, Herbert often relies on language that represents sin as a numerical part of God's economy of grace as well as imagery that portrays man's transgressions as held within a room or compartment in his heart.The paper concludes that throughout The Temple, Herbert must continually reject these material forms of sin to come to a better understanding of his God.Dans The Temple, George Herbert utilise une foule d'images surprenantes et stimulantes pour le péché.Dans «Sin (2)», il semble adhérer à la notion théologique du mal en tant que privation ; pourtant, dans nombreux de ses poèmes, il donne au péché une présence matérielle qui mène à la fixation.Cet article établit un lien entre les représentations du péché chez Herbert et le scrupule.Il soutient qu'un malaise à l'égard de l'immatériel incite plusieurs de ses locuteurs à dépeindre leurs méfaits d'une manière qui atténue leurs inclinations scrupuleuses à rendre les réalités spirituelles visibles et mesurables.En particulier, Herbert s'appuie souvent sur un langage qui représente le péché comme une partie numérique de l'économie de la grâce de Dieu, ainsi que sur une imagerie qui dépeint les transgressions de l'homme comme contenues dans une pièce ou un compartiment dans son cœur.L'auteur conclut que tout au long du Temple, Herbert doit continuellement rejeter ces formes matérielles du péché pour parvenir à une meilleure compréhension de son Dieu.and revisions made this work possible.My graduate experience was often shaped by her great kindness as well as her inexhaustible joy in literaturethere were times when these made all the difference.I'd also like to thank Prof. Brian Trehearne, Prof. Michael Van Dussen, and Prof. David Hensley for their support, mentorship, and teaching.So many of the conversations we shared will not be forgotten, and I hope to carry their insights and warmth with me as I now venture beyond these "reverend walls."To George Herbert, of course, I owe a lot: thank you for your honesty and for giving us the words we didn't know we needed: I read, and sigh, and wish I were a tree.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".