Bibliographic record
Abstract
This paper presents puzzles concerning the representation of features in the agreement system of the Eastern Algonquian language, Mi’gmaq. A growing body of research converges on the idea that φ-agreement should be separated into distinct person (π0), number (#0), and sometimes gender (γ0) probes (e.g. Anagnostopoulou 2003, Béjar 2003, Béjar and Rezac 2003, Laka 1993, Shlonsky 1989, Sigurðsson 1996, Sigurðsson and Holmberg 2008, Preminger 2012). While these proposals account well for agreement and partial agreement patterns in a number of languages, we show that in order to account for the agreement system of Mi’gmaq, π0 and #0 must probe together, which we argue to be the result of fusion of two distinct probes. We discuss the implications of Mi’gmaq agreement for “prominence hierarchies” and feature geometries in the grammar.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".