Bibliographic record
Abstract
In 2003 I took a trip to Brazil for the first time after discovering my runaway mother’s whereabouts by accident on the Internet. I had not seen nor heard from her since I was eight years old, but I boarded a plane in Toronto and landed, without language and with little knowledge of Brazilian culture, in Sao Paulo, where she was working. I spent ten turbulent days with her and then we visited Brasilia where the rest of my Brazilian family awaited my own strange reappearance in their lives. “Brazil, Under These Circumstances” is aseries of poems that exploreshow place is affected by our imaginative perceptions of our families. In much the same way that when we are disoriented we look for landmarks to stabilize ourselves, the speaker attempts to navigate her way through the family drama by accessing translated confessions, troubled memories, even absurd adventures. Brazil, like a runaway mother,constantly changes,from exotic jungle to criminal haven to government town to a treasure trove of garbage. The purse image, which repeats prominently several times in the titles of poems, also changes throughout the poetic sequence.Can identity be bought? Can memories be mugged? Who does a place belong to? Who does a mother (or,for that matter, a grandmother or an uncle) belong to? The landscape in these poems undergoes constant negotiation within the speaker’simagination. And that negotiation is still ongoing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".