Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it