How multiple past tenses divide the labor: The case of South Baffin Inuktitut
Bibliographic record
Abstract
Abstract It is a common perception that in languages having multiple past tenses with different remoteness specifications, the past tenses cover the entire past without a gap or overlap. This paper demonstrates that this way of looking at multiple-past tense systems is not appropriate for the system in South Baffin Inuktitut (a variety of the Inuit language). The dialect has at least four past tenses: recent, hodiernal, pre-hodiernal, and distant. We argue that the relation between the four tenses cannot be represented by a simple linear scheme for two reasons. First, the pre-hodiernal past has a special status as the “conventionally designated alternative”, which is chosen in cases of remoteness indeterminacy, analogous to, for example, the Russian masculine gender being used in cases of gender indeterminacy. Second, there is overlap in their coverage. The pre-hodiernal and hodiernal past tenses collectively cover the entire past and thus any past situation can be described with one of them. The other two provide means to make more fine-grained and subjective temporal specifications. Comparison will be made between the system in South Baffin Inuktitut and those in some Bantoid languages which have been pointed out in the literature to have a comparable layered system of tenses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".