Bibliographic record
Abstract
This chapter investigates the mass/count distinction in Michif. In many languages, mass and count nouns are distinguished via the (in)ability to occur with plural marking, the (in)ability to occur with numerals without a measure phrase, and the (in)ability to occur with certain quantifiers (Jespersen 1909; Chierchia 1998). However, these diagnostics do not apply to all languages. For example, in Inuttut (Labrador Inuktitut), none of those diagnostics distinguishes between mass and count nouns, but there are other diagnostics that do (Gillon 2012). This chapter shows that Michif displays a split: in one part of the grammar, the three diagnostics distinguish between mass and count nouns, and in another part, the diagnostics do not. This shows that Michif disambiguates between French-derived vocabulary and Algonquian-derived vocabulary, which complicates the notion that the Michif DP is French (Bakker 1997).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".