Bibliographic record
Abstract
Some languages allow the arguments of a verb in a not to be expressed as an overt Pronoun [Lexicon- Section 3.7] or a lexical [Syntax- Chapter 4]. This is the situation in which the term ‘null argument’ is commonly used. Spoken languages vary with respect to whether they allow the arguments of the verbs to be Miller, C. 1994. Simultaneous Constructions in Quebec Sign Language. In: Brennan, M. & G.H. Turner (eds.), Word-Order issues in sign language. Durham: International Sign Linguistic Association, 89-112. tensed clause noun phrase silent.Null arguments are most commonly observed in languages like Italian, Spanish, Catalan and Turkish which have a rich verbal agreement morphology. English, on the other hand, which does not have a rich verbal morphology does not allow arguments of a predicate to be phonologically null in a sentence. In the Turkish and Catalan examples below, the verb bears the person and number agreement marker for the subject which is not phonologically expressed (pro indicates the phonologically null pronoun). a. Kitab-ı bitir-di-m book-ACC finish- PAST-1SG ‘I finished the book.’ b. Al camp pro ho aprofiten tot. in-the countryside it use.3PLeverything ‘In the countryside they use everything.’ (Turkish) (Catalan, Barbera & Quer 2013: ex. (1a)) Languages which identify the referent of the null argument by means of verbal agreement morphology are said to use a licensing strategy based on agreement. Similar to spoken languages, many sign languages also allow one or more of the arguments of the verb in a tensed clause to be phonologically unexpressed. In the ASL question-answer exchange below, the agreeing verb send is marked for subject and object agreement. DID JOHN SEND MARY THE PAPER? YES, ASENDB ‘Yes, (he) sent (it) to (her).’ (ASL, Lillo-Martin 1986: 421) As can be observed, neither the subject nor the object argument of the verb send is pronounced in the response. The null pronouns are nevertheless interpreted as a definite pronominal such as he, her, and it.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.146 | 0.067 |
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".