09. Recipes in Malagasy and Other Languages
Bibliographic record
Abstract
This paper looks at null arguments in recipe contexts.While much of the literature has focused on English and the availability of null definite patients, this paper shows that null agents and null patients are possible in recipes in a range of languages, including Malagasy, Niuean and Tagalog.It is argued that null agents in recipes arise due to a variety of syntactic strategies, but null patients are licensed via a null topic in all the languages considered.* For an earlier version of this paper, see Paul and Massam (to appear).We would like to thank our language consultants, Vololona Rasolofoson, Ofania Ikiua, and Lynsey Talagi.We have also benefitted from feedback from Edith Aldridge, Kazuya Bamba, Henrison Hsieh, Yves Roberge, Vesela Simeonova, Rob Stainton, Michelle Troberg, and audiences at the Canadian Linguistic Association and the Austronesian Formal Linguistics Association meetings.All errors remain our own. 1 Following many others (see references), we use the term "register" rather than "genre".Nothing crucial hinges on this terminology, however.See Ferguson (1994) for a discussion of these terms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".