Bibliographic record
Abstract
How Forests Th ink has been gestating for some time, and I have many to thank for the life it has taken.I am indebted foremost to the people of Ávila.Th e times I spent in Ávila have been some of the happiest, most stimulating, and also most tranquil I have known.I hope that the sylvan thinking I learned to recognize there can continue to grow through this book.Pagarachu.Before I even went to Ávila, my grandparents the late Alberto and Costanza Di Capua had already prepared the way.Italian Jewish refugees settled in Quito, they brought their curiosity to everything around them.In the 1940s and 1950s my grandfather, a pharmaceutical chemist, participated in several scientifi c expeditions to the Amazon forests in search of plant remedies.My grandmother, a student of art history and literature in Rome, the city of her birth, turned to archaeology and anthropology in Quito as a way of understanding better the world into which she had been thrown and which she would eventually call home.Nonetheless, when I returned from my trips to Ávila she would insist I read to her from Dante' s Divine Comedy while she fi nished her evening soup.Literature and anthropology were never far removed for her or for me.I was twelve years old when I met Frank Salomon in my grandmother' s study.Salomon, a scholar like no other, and the person who would eventually direct my PhD research at Wisconsin, taught me to see poetry as ethnography by other means and so opened the space for writing about things as strange and real as thinking forests and dreaming dogs.Th e University of Wisconsin-Madison was a wonderful environment for thinking about the Upper Amazon
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.505 | 0.356 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".