Landscapes of Trauma and Transformation: Poems of Anne Michaels, Karen Shenfeld, and Miriam Waddington
Bibliographic record
Abstract
Long before ecocriticism became a focus for textual analysis, poets articulated interconnection with the natural world. Not only green but vibrant colour across the spectrum has semiotically served poets as they sought to venture ever deeper into understanding the expanse of human experience and the anguish that all too often accompanies it. The idea of post-traumatic transformation is rooted in the Hebrew Bible and in the contextual stories with which it is intertextually connected. Casting out the idea that hardship is intentionally imposed for the express purpose of causing growth, the fact remains that suffering—sometimes unbearable suffering—is integral to human life and we must grapple with it. The colourful imagery and synesthesia of landscape can be leveraged descriptively and metaphorically to express pain, confusion, and questioning. Traversing the terrain of confounding events, poetic poesis of the works analyzed in this article demonstrates a wrestling to find, however provisionally, transformative trails through the wreckage.Bien avant que l’écocritique ne devienne un focus pour l’analyse textuelle, les poètes ont articulé l’interconnexion avec le monde naturel. Plus que la couleur verte, tous les tons à travers le spectre ont servi sémiotiquement les poètes alors qu’ils cherchaient à s’aventurer toujours plus profondément dans la compréhension de l’étendue de l’expérience humaine et de l’angoisse qui l’accompagne trop souvent. L’idée de transformation post-traumatique est enracinée dans la Torah et dans les histoires contextuelles avec lesquelles elle est intertextuellement connectée. En rejetant l’idée que la souffrance est intentionnellement imposée dans le but de provoquer la croissance, il n’en reste pas moins que la souffrance—parfois insupportable—fait partieintégrante de la vie humaine et nous devons la combattre. L’imagerie colorée et la synesthésie du paysage peuvent être exploitées de manière descriptive et métaphorique pour exprimer la douleur, la confusion, et le questionnement. Traversant le terrain d’événements déroutants, la poésie poétique des oeuvres analysées dans cet article démontre la lutte pour trouver, même provisoirement, des pistes transformatrices à travers les épaves.
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 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 | 0.013 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".