Bibliographic record
Abstract
Word order in the Inuit language is relatively “free” (cf. Dorais 2010). However, there seems to be a consensus that the neutral order is SOV with oblique arguments appearing between the object and the verb, and that any deviation from this is triggered by discourse and stylistic factors (see Fortescue 1984, 1993, Tersis & Carter 2005). For example, arguments that represent new information or are heavy would tend to appear post-verbally. Furthermore, by comparing short texts collected between the 1820s and the 1970s, Fortescue (1993) shows that non-neutral word orders like SVO have become more frequent in more recent texts across all varieties, arguably due to contact with strict-SVO languages like English and Danish, which have had a strong influence over the Inuit population since the start of the 20th century. Yet previous studies on word order in the Inuit language display shortcomings. First, the statistical significance of the proposed factors has never been evaluated in any Inuit variety. Further, there is little detail given on what makes an argument heavy. As for the possible contact-induced change on word order, Fortescue (1993) acknowledges that the small size of his corpus and the varying characteristics of the texts may have skewed the results. This paper presents a study on word order in North Baffin Inuktitut based on a large corpus and using variationist sociolinguistic methods and shows that 1) the rise of Inuktitut-English bilingualism has in fact not affected word-order patterns in this dialect, 2) heavier arguments tend to appear post-verbally but newly-introduced ones are surprisingly favored pre-verbally and 3) oblique arguments surface after the verb significantly more often than subjects and objects, which are claimed to be topicalized arguments in the Inuit language (see Berge 2011, Carrier 2021). Given these results, I argue that word-order variation in Inuktitut is a stable variable mainly conditioned by information structure but also influenced by utterance planning processes (see Stalling & McDonald 2011).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".