Bibliographic record
Abstract
This paper accounts for the strategies that Yorùbá adopts to mark plural. One way in which plural is marked syntactically is by certain plural words. The plural word can either interpret the noun as plural directly as in the case of àwọn and quantifying words such as púpọ̀ ‘many’ and méjì ‘two’; or it can be realized on a primitive adjective (in the form of COPY) or on a demonstrative (in the form of wọ̀n-). Such elements in turn make available the plural interpretation of the noun they modify. The paper proposes that these plural words possess a covert or an overt [PLURAL] feature, which percolates onto the NP. This analysis of plural marking predicts that there are two ways by which languages may (overtly) mark their nouns for plural cross-linguistically. Languages like Yorùbá, which do not show agreement, mark plural syntactically and make use of a plural feature percolation mechanism, while languages like English, which show agreement, mark plural morphologically and use a plural feature-matching mechanism. It further demonstrates that in Yorùbá, an NP can be freely interpreted as singular or plural in specific discourse context and proposes a general number analysis to account for this type of case. As to the syntax of these plural words, It is proposed that like other non-morphological plural marking languages (e.g., Halkomelem (British Columbia, Canada) as in Wiltschko 2008), Yorùbá plural words are adjuncts that are adjoined to the host head (noun or modifier/demonstrative).
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".