The Emergence of the Marked: Root-Domain Markedness in Lakhota
Bibliographic record
Abstract
this paper: call attention to a less-discussed pattern, in which strong positions have systematically simpler structures than weak positions (6) Lakhota: codas are generally banned in roots, but they may surface in affixes, function words, and reduplicants (data below) Proposal: such patterns are best captured by markedness constraints whose domain in the root (RSCs) (7) Outline of the paper . Description of Lakhota: possible roots, affixes, clitics, and reduplicants . The same set of codas is allowed everywhere . . . except roots . Root-domain Structure Constraints: markedness constraints whose domain of application is the root (or lexical category) . Discussion: the motivation of root-specific constraints on syllable structure, and some unresolved issues 2 The distribution of codas in Lakhota (8) Preliminaries on Lakhota . Siouan language, spoken primarily in N. and S. Dakota, Nebraska, Minnesota, and Canada . Data from Boas and Deloria (1941), Buechel (1970), Shaw (1980), Munro (1989), and field work with a native speaker, Mary Rose Iron Teeth . Consonant inventory unaspirated p t tS k aspirated p h1 ejective p' t' tS' k' fricatives s, z, s' S, Z, S' x, G, x' nasals m n N liquid l glides j w (9) Lakhota words allow a fairly rich set of onsets (A representative selection) Stop + stop: pte `cow' tke `heavy' a `but' kt u `wear' Stop + fric/affric: ps `rice' pSa `sneeze' kSto (emph. clitic) tSi `with' Fric + stop: xttu `evening' xp `lie down' stu `in love' Skate `play' Obstruent + sonorant: blo `potato' gli `arrive home' gn `cheat, fool' sni `cold' Nasal + nasal: mni `water' (10) Word-final codas are generally not permitted . (*[kat], *[tax], *[man]) (11) Word-medially: arguably also no codas . Same clu...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".