Bibliographic record
Abstract
The thirty-six lessons are divided into two parts. The sixteen that comprise the first part cover the most basic elements of the language, and at the end of this section, there is an extensive set of review exercises to help make sure that you are in full command of the fundamental elements of the language before proceeding to the finer points dealt with in the second part. Each lesson also has a large number of exercises. These are very thorough in the earlier lessons, with a particular emphasis on acquiring an active knowledge of the forms. Experience shows that students have an aversion to translating from English into the language being learned. Naturally, it is more difficult to produce the forms rather than simply converting the Nahuatl sentences into English, but it is the very act of manipulating the language through active composition that allows you to understand the forms and their uses. A key to the exercises is provided in Appendix Four, but you are strongly advised to avoid using it until you have finished all the exercises and have done your best to find the solution to something you are having trouble with in the lesson (many fine points in the exercises can be figured out this way). Each lesson comes with a list of vocabulary items at the end, and these should be memorized. Those who have learned all these words should have a very good basic vocabulary at their fingertips when they go on to reading texts. The English-Nahuatl vocabulary at the end of the book is a full listing of all the words in the vocabularies of the individual lessons, but the Nahuatl-English is limited to words useful for completing the English-Nahuatl exercises.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.210 | 0.229 |
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".