Bibliographic record
Abstract
I read some essays on linguistic relativity while I was in high school in the late 1960s. I took the fundamental idea to be that people in different societies could receive and operate within somewhat different construals of the world while still being human, intelligent, and competent, and that these received construals could be related to the specifics of their diverse languages. These ideas seemed worthy of pursuit, and I was surprised over the next twenty years to find linguistic relativity almost uniformly disrespected by linguists and psychologists, and often by anthropologists. This book is an attempt to understand both the ideas behind linguistic relativity and the scorn the concept provokes in so many quarters. “The tale grew in the telling,” wrote J. R. R. Tolkien at the beginning of The Lord of the Rings , and while my ambition as a teller has been much more modest than his, this tale, too, has been many years a-growing. I particularly want to thank Paul Friedrich for years of inspiration and support, and Claude Faucheux, Mark Mancall, and Kevin Tuite for their encouragement and good ideas. The book is dedicated to my father, Harold J. Leavitt; I like to think that he would have gotten a kick out of it. It has benefitted from the specific comments of Bernard Bate, Gilles Bibeau, Pietro Boglioni, Bernard Chapais, Robert Crépeau, Regna Darnell, Jean DeBernardi, David Dinwoodie, Johannes Fabian, Michel de Fornel, Kellie O'Connor Gutman, Douglas Hofstadter, Dell Hymes, Maggie Kilgour, Friederike Knabe, Konrad Koerner, Guy Lanoue, David Leavitt, Penny Lee, Gérard Lenclud, Jean Lipman-Blumen, John Lucy, Bruce Mannheim, Margaret Paxson, Emily Schultz, Mary Scoggin, Sonia Sikka, Michael Silverstein, Pierrette Thibault, Jürgen Trabant, and Francis Zimmermann.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.279 | 0.162 |
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".