General Extenders in Persian Discourse: Frequency and Grammatical Distribution EXTENSION GÉNÉRALE DANS LE DISCOURS EN PERSAN: FRÉQUENCE ET DISTRIBUTION GRAMMATICALE
Bibliographic record
Abstract
Abstract: This study tries to investigate frequency and grammatical distribution of general extenders in Persian. The analysis is based on a corpus of informal conversations. On some occasions, a comparison will also be made with corpus of informal English compiled and analyzed by Overstreet (1999, 2005). The results of this study lay bare fact that Persian speakers use adjunctive general extenders more frequently than disjunctive ones. It will also be demonstrated that Persian speakers use general extenders both at clause final and clause-internal positions. Finally, Persian general extenders will be examined with reference to their grammatical agreement requirements. Keywords: Discourse marker; frequency; general extender; grammatical distribution; Persian Resume: Cette etude tente d'etudier la frequence et la distribution grammaticale connues sous le nom de l'extension generale en persan d'un groupe de locuteurs. L'analyse est fondee sur un corpus de 9 heures de conversations informelles. Dans certains cas, une comparaison sera egalement faite avec le corpus en anglais compile et analysee par Overstreet (1999, 2005). Les resultats de cette etude mettent a nu le fait que les locuteurs persans utilisent plus souvent les extensions generales sulbaternes que les extensions gererales adversatives. En outre, l'analyse revele que les locuteurs persans ne modifient pas leur extension generale avec un element comme adverbe. Il sera egalement demontre que les locuteurs persans utilisent les extensions generales a la fois aux positions de clause finale et de clause interne. Enfin, les extension generales perses seront examinees en ce qui concerne leurs besoins en accord grammatical. Mots-cles: locuteurs de discours; frequence; extension generale;distribution grammaticale; perse (ProQuest: ... denotes non-USASCII text omitted.) INTRODUCTION As stated by Channell (1994), people hold many beliefs about language they speak. The most important one is that good usage involves, inter alia, clarity and precision. Accordingly, it is believed that vagueness, imprecision, and general woolliness are to be avoided. However, as argued by Channell (1994), it is rather too simple a view, and likely to be misleading not only for those who speak a particular language as their mother tongue but also for those who are making an effort to learn a new language other than their first language. Perhaps it was Peirce (1902), who, for first time, introduced notion of vagueness in linguistic studies. He was of opinion that a proposition is vague where there are possible states of concerning which it is intrinsically uncertain whether, had they been contemplated by speaker, he would have regarded them as excluded or allowed by proposition. It is worth noting, however, that by intrinsically uncertain he does not mean uncertain in consequence of any ignorance of interpreter, but because the speaker's habits of language were indeterminate; so that one day he would regard proposition as excluding, another as admitting, those states of things (p. 748). In keeping with above mentioned observation, it has, for too long, been acknowledged that vague language occurs widely in language use so much so that some investigators have wished to maintain that all language use is vague in some way (see Channell, 1994; Cutting, 2007). Since introduction of notion of vagueness in linguistics by Peirce in 1902, a great many number of scholars have tried to define vague language in one way or another (see Ball & Ariel, 1978; Crystal & Davy, 1975; Cutting, 2007; Deese, 1974; Wierzbicka, 1986). Even so, most comprehensive conceptualization of vague language seems to have been provided by Channell (1994, p. 20), who contends that an expression or word is vague if: A it can be contrasted with another word or expression which appears to render same proposition; B it is purposely and unabashedly vague; C its meaning arises from intrinsic uncertainty referred to by Peirce. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".