Person and deixis in Heiltsuk pronouns
Bibliographic record
Abstract
Abstract Harbour (2016) argues for a parsimonious universal set of features for grammatical person distinctions, and suggests (ch. 7) that the same features may also form the basis for systems of deixis. We apply this proposal to an analysis of Heiltsuk, a Wakashan language with a particularly rich set of person-based deictic contrasts (Rath 1981). Heiltsuk demonstratives and third-person pronominal enclitics distinguish proximal-to-speaker, proximal-to-addressee, and distal (in addition to an orthogonal visibility contrast). There are no forms marking proximity to third persons (e.g., ‘near them’) or identifying the location of discourse participants (e.g., ‘you near me’ vs. ‘you over there’), nor does the deictic system make use of the clusivity contrast that appears in the pronoun paradigm (e.g., ‘this near you and me’ vs. ‘this near me and others’). We account for the pattern by implementing Harbour's spatial element χ as a function that yields proximity to its first- or second-person argument.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".