The Second Collection of Prose Poems “The Love Season of Leaves”
Bibliographic record
Abstract
“The Love Season of Leaves” is the second collection of leaves written in prose-poem forms. The author supposes that these collections are Romantic prose-poems she names Magic Naturism because such subjective nature is more characterized and personified than nature in some other romantic works. Alive, animated, and dynamic nature is an essential doer, playing its roles alongside with humans and the other species. Moreover, magic, marveling, and what people call healing or psychic physical balance occur in the heart of nature in alliance with other species. There are celebrations of magic in natural phenomena such as rain, cloud, breeze, ponds, soil, rivers, and forests surrounding mini-narrations of humans’ predicaments. Edenic childhood, love, death, rebirth, drought, glory of art, escapism, sexism, racism, speciesism, slavery, war and its ruins, poverty, solitude, and sublime are local/ universal themes that may be repeated in collections, but their variety of forms, lines, points of view, and styles represent new pieces of writing. Paradox, alliteration, allusion, multiple-metaphors, sensory description, repetition, symbolism, and other literary tropes in this collection are not just tools of literary writing. These collections are social/environmental based prose-poems attempting to expose hidden realities and fleeting fantasies. The second collection represents cultural social factors of particular areas of Iran, Wales, Scotland, Canada, The USA, Afghanistan, Azerbaijan, India, Sweden, and Japan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".