Bibliographic record
Abstract
Abstract The study of polysynthesis has a long history; but defining it remains a challenge and many researchers have suggested that polysynthesis is not an absolute or unified phenomenon. In this entry, drawing largely on examples from Inuktut and Blackfoot, two Indigenous languages of Canada, we describe the range of grammatical properties that have been proposed to characterize polysynthetic languages. We show that both languages possess all the properties canonically associated with polysynthesis – holophrasis, rich phi‐marking, noun incorporation, lexical affixes, morphological complexity and non‐configurationality; yet they exhibit variation in these properties, as well as in their morphological organization. The entry highlights this variation by contrasting Blackfoot and Inuktut, and also other languages often described as polysynthetic, including Kanien'kéha (Mohawk). We outline theoretical approaches to polysynthesis, focusing on analyses of rich phi‐marking and wordhood and drawing on data from a diverse range of languages. The entry concludes with a discussion of practical challenges for polysynthetic languages, with a focus on Indigenous language revitalization. We suggest that, despite not having a clear and unified theoretical definition, the notion of polysynthesis is useful in language revitalization, particularly for language teachers and learners and those involved in language resource development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".