Bibliographic record
Abstract
“Preguntas y frases” is an imagined letter from my grandmother. It is composed of Spanish words and phrases (including missing accents and misspellings) as my grandmother, Esther, wrote them in 1983 in a series of letters she sent to my mother while my mother was living abroad in Ecuador. In those letters, Esther spoken plainly with her daughter—principally, by questioning her decision to leave the United States and asking that my mother back home to Calexico, California. Many years after my grandmother’s death, my mother found these letters tucked away in the house attic. When my mother shared these letters with me, it was startling for me to see and hear so clearly my grandmother’s voice and way of speaking after so many years since her passing. In response, I wrote “Preguntas y frases para una nieta americana.” The title is inspired by Teresa Palomo Acosta’s poem “Preguntas y frases para una bisabuela española” in which Acosta reflects on her Spanish heritage by writing a letter to her great Spanish grandmother. In contrast, “Preguntas y frases para una nieta americana” reflects on American assimilation and what is lost, protected, and honored across three generations of Mexican American women. The letter is my imagination of what my grandmother would say to me today if she were to write me—and speak plainly—as she had with my mother all those years ago.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".