Bibliographic record
Abstract
Whenever linguists discover that I work on Athapaskan languages, I can anticipate the first question that they will ask – however could a child come to learn the order of morphemes in the verb of one of these languages? The order of morphemes seems to be completely without rhyme or reason. Morpheme order is thus a question that is everpresent in one's mind when studying languages of this family. I first began to tackle this problem in 1991, with work on the so-called disjunct prefixes of the Athapaskan verb. A crosslinguistic survey revealed something very striking – little variation existed across the family in terms of the ordering of these elements. I began to feel that I was on the road to an explanation of the ordering of this part of the verb, but the so-called conjunct portion of the verb still left me baffled. One day in the early 1990s Chomsky gave a talk here at the University of Toronto, and I began to get some glimmerings; at least the ordering began to look somewhat less random than it had hitherto seemed. It was after this that I decided that this was a research question that I had to pursue. The quest to come to some personal understanding of morpheme order took me several years, as there was much I had to learn in many different arenas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.397 | 0.228 |
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".