Bibliographic record
Abstract
This paper questions the connection between bare nouns, incorporation and obligatory narrow scope.Data from Malagasy show that bare nouns take variable scope (wide and narrow) despite being pseudo-incorporated.The resulting typology of incorporation is presented and two analyses of the Malagasy data are explored.The paper concludes with a discussion of the nature of incorporation and indefiniteness.* This research would not be possible without the input from several native speakers of Malagasy: Rita Hanitramalala, Jean Christophe Jaonesy, Tsiorimalala Randriambololona, Vololona Rasolofoson, Francine Razafimboaka, Martelline Razafindravola, and Rado Razanajatovo.I would also like to thank Sandy Chung, Lisa Matthewson, Hotze Rullmann, as well as audiences at UBC, at the Mass/Count workshop at the University of Toronto, and at AFLA XVI at UC Santa Cruz for
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".