Bibliographic record
Abstract
As this book unfolds, we will see that the verbs of the Athapaskan family exhibit two attributes, outlined in chapter 1. First, global uniformity exists – morpheme order is similar across the family. Second, so does local variability – some variability in morpheme order occurs both within a language and across the family. In this chapter I examine some possible hypotheses to account for both of these properties. Two Hypotheses One can imagine different ways of accounting for the fact that the Athapaskan language family as a whole exhibits both global uniformity and local variability. I outline two here. Under both hypotheses, global uniformity has its origins in the languages having a common source, but they differ as follows. Under one hypothesis, it is the common source and history of the languages that accounts for the global uniformity. It is an accident of history that certain properties remained stable across the language family while others were subject to change within individual languages. I call this the template hypothesis. Under a second hypothesis, global uniformity finds its origins in the languages having a common source, but results additionally from principles of universal grammar, which might be either diachronic or synchronic. I call this the universal or scope hypothesis. In this chapter I examine why certain aspects of verb morphology are invariant while others have been susceptible to change, both across languages and within a particular language.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".