Bibliographic record
Abstract
Overview of Metchif Adjectives 101 AN OVERVIEW OF METCHIF ADJECTIVES Richard A. Rhodes Associate Dean, Undergraduate Division College of Letters and Science University of California rrhodes@berke|ey.edu ABSTRACT Metchif is well known to have its vocabulary and syntax split between French and Plains Cree. At first approximation, the verbs are from Cree and the noun phrase is from French, including the adjectives. This paper presents a detailed account of adjectives and adjective phrases in Metchif. Resume Le vocabulaire et la syntaxe du mitchif. on ie salt, sont repartis entre le fran- gals et le cris des plaines. En gros on peut dire que les verbes sont issus du cris des plaines et le syntagme nominal, y compris les adjectifs, du francais. Cet article offre une analyse detalllee des adjectifs et des syntagmes adjecti- vaux en mitchif. Metchif is well known to have a vocabulary (and syntax) split between French and Plains Cree (Rhodes 1976, Bakker 1997, among others). At first approximation, the verbs are Cree and the nouns are French. More ac- curately the noun phrase is French, although Cree deictics are used. Oth- er than the nouns and articles, the other French components of the noun phrase are the adjectives. There is only a little in the literature on Metchif adjectives. There is a brief section in Rhodes (1977) preliminary grammar sketch. A similarly brief section is given in Bakker (1997). A slightly fuller dis- cussion can be found in Bakker and Papen (1996). The purpose of this pa- per is to give an expanded discussion of the points raised in those works. The basic noun phrase construction in Metchif is summarized in (1). (1) Noun phrase structure‘ NP = determiner - quantifier — article — pre-nominal modifier(s) — N — post- nominal modifier(s) — relative clause Examples showing forms in the various slots are given in (2).? The Canadian Journal of Native Studies XXXIII, 2 (2013)
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".