Types of pluractionality and plurality across domains in ʔayʔajuθəm
Bibliographic record
Abstract
In this paper, we examine two markers of verbal plurality, C1C2 reduplicationand ablaut, in PayPaju8@m, a Central Salish language. C1C2 reduplicationmarks event external pluractionality, where subevents are distributed in both spaceand time. It also applies in the nominal domain creating a plurality of individuals, butdoes not impose temporal or spatial distribution in the nominal domain. FollowingHenderson (2012, 2017), we propose that events are individuated through their temporaland spatial traces, so that events distribute in order to pluralize, whereas thisis not required in the nominal domain. Ablaut marks event-internal pluractionalitywhere subevents are grouped into a larger whole (Wood 2007; Henderson 2012,2017). While ablaut pluractionals typically involve numerous subevents that areclosely spaced in time, they can involve as few as two subevents and do not requirestrict adjacency of all subevents. We propose that they denote an atomic groupevent that is mapped to a plurality of events via a membership function (Barker1992). This contrasts with event-internal pluractionals that require a high number oftemporally adjacent subevents and have been analyzed as being grouped throughtheir temporal configuration (Henderson 2012, 2017), indicating that there is morethan one way to group events, just as there is more than one way to group individualsin the nominal domain (Barker 1992; Henderson 2012, 2017).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".