Bibliographic record
Abstract
In the summer of 1922 Edward Sapir visited the Sarcee Reserve and recorded in seven volumes of notebooks lexical and textual material as narrated by John Whitney.It is this body of material that constitutes the basic data of this book.In 1966, almost half a century after Sapir's field work, I was fortunate to be acquainted with Sapir's material thanks to two of his students who became eminent scholars in Amerindian studies -the late Professors Morris Swadesh and Harry Hoijer -who made arrangements with the help of Victor Golla to make available to me through the University of Alberta, a copy of Sapir's lexical files and grammar files.Professor Hoijer later lent me Sapir's original field notes, from which a photocopy was made, and left me the original typed version of texts.If I had not had the privilege of studying these materials, I could not have started the book.Although I was fascinated by Sapir's field notes when I first saw them, it was not until I had acquired a few years first-hand experience that I came to appreciate the accuracy of his transcriptions and translations and the depth of understanding of the language that he had obtained in a short period of time.Having used so much of his material in writing this book, I am afraid that I may have inadvertently misrepresented him.The initial plan for this book was made in 1972 when I was awarded a Killam Senior Research Scholarship by The Canada Council.This scholarship enabled me to
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.551 | 0.296 |
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".