Bibliographic record
Abstract
Pre-MorningWhose image did you see In the pre-morning paling moon young girl, tender as the first shoots of dawn?Dream-addled little daughter of the steely-eyed, handle-bar moustache, whose voice accompanied you, walking to the fields before sunrise in a place where childhood had yet to be invented?If Jung is right that children are psychologically burdened with their parents' unlived lives, better to make poetry of it, I say-especially the weighty, more painful, parts.My poem, "Pre-Morning," evokes my mother's desolation as a young child walking alone in the pre-dawn darkness to meet her father and begin a long day's work.The scenic Calabrian town of my birth was bordered by cultivated fields, age-old olive groves and fragrant fruit orchards.During planting season, my grandfather sometimes worked late into the evening and stayed overnight, sleeping in a shed built for shelter from extreme weather and other contingencies.By the age of eight, my mother was expected to arrive and begin her labours at sunrise.In my mid-forties, while examining my own upbringing, I realized that the concept of childhood was missing from my mother's parenting toolbox."Pre-Morning" acknowledges my mother's hapless upbringing in a place marked by childhood's absence; a place that is not only a physical locale of struggle and necessity, but one that includes my grandfather's harsh world view.His mental landscape appears in another one of my poems, "Learning How Not to Dream," about a cajoling letter my mother wrote
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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 | 0.002 | 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 | 0.020 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".