Bibliographic record
Abstract
Ojibwe is an Algonquian language spoken around the Great Lakes region of the United States and Canada. It has grammatical gender and a classifier system, which are rare in a single language (Corbett, 1991:137; Fedden and Corbett, 2017). I provide a detailed and typologically-informed analysis of numeral and verbal classifiers in Ojibwe. Numeral classifiers can be of two types: mensural, referring to measurements, and sortal, referring to properties such as dimensionality, size, and material. It is shown that these types can be distinguished by occurring with differing forms for the numeral 'one', and sortal classifiers are vital to understanding gender assignment. Assignment is mostly straightforward, with all nouns denoting humans and animals in the ANIMATE category, and the vast majority of nouns denoting inanimates in the INANIMATE category. However, some nouns with inanimate referents are ANIMATE. Previously characterized as 'exceptions' to semantic assignment, they are motivated by compatibility with the semantics of one of these sortal classifiers, as illustrated by pairings of classifiers and nouns (1). I also discuss the role of analogical extension, dialectal variation, diachronic change and claims for an interaction of gender with the count/mass distinction. 1. a. /-aatig/ '1D, rigid', i.e. stick-like - mitig 'tree' b. /-aabiig/ '1D, flexible', i.e. string-like - zesab 'nettle' c. /-eg/ '2D, flexible', i.e. sheet-like - asekaan 'tanned hide' d. /-minag/ '3D, small, round', i.e. berry-like - miskomin 'raspberry' e. /-aabik/ 'mineral', i.e. metal, stone, glass - asin 'a stone'
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".