Bibliographic record
Abstract
strange resonances of intergenerational connection. Bleach is an autobiographical reflection that explores the strange resonances of intergenerational connection. The author considers how the memory of her grandmother—and the transmitted stories of her grandmother’s own childhood—can be seen anew through her own apprehension of growing old. “As I nurse my own hair’s / whitening, I coax age through / and out of me—my body, in time, entangling / these hard traces of the past.” Integrating images of bleaching and stains, farming and public health, depression and growth, Bleach braids together past and present in its search for “lessons / on how to grow old.” In “Fire Fallow,” the author draws on the conventions of prose poetry to explore the phenomenological nature of mental health and illness, burnout especially. Using the historical catastrophe of the volcanic eruption of Pompeii as a conceit, Fire Fallow explores the terrible paradoxical intimacies of creation and destruction, the chronic fiction of a work-life balance. Turning away from false comforts--the insufficient language of resilience, regrowth, and regarging--this poem grapples with the reality of health care work and life with a clear-eyed lesson: What lights our way will burn our skin and scorch the earth.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".