Number Marking in English and Thali: A Contrastive Study
Bibliographic record
Abstract
The aim of this paper is to explore the number system in Thali, a variety of Punjabi spoken by natives of Thal desert. There are three number categories singular, dual, and plural but all modern Indo Aryan languages have only singular and plural (Bashir & Kazmi, 2012, p. 119). It is one of the indigenous languages of Pakistan from the Lahnda group as described by Grierson (1819) in his benchmark book Linguistic Survey of India. Layyah is one of the prominent areas of Thal regions. The native speakers of Thali use this sub dialect of Saraiki in their household and professional life. The linguistic boundaries of the present Siraiki belt have changed under different linguistic variational rules as described by Labov (1963), Trudgal (2004), Eckert (2002) and Meryhoff (2008). There are many differences between Thali and Saraiki, on phonological, morphological and orthographical levels. Husain (2017) has pointed out linguistic differences between Saraiki and Lahnda and Thali is one of the popular languages of Lahnda spoken in different parts of Thal regions. According to the local language activists, Thali has been greatly influenced by Saraiki and Punjabi. The lexicon of Thali is composed for 20% of Punjabi, 45% of Saraiki, and 5% of loan words particularly English. Another particularity is that Perso-Arabic characters are used to write Thali. The most distinguishing characteristics of Thali are its parts of speech, word order, case marking, verb conjugation and, finally, usage of grammatical categories in terms of number, person, tense, voice and gender. In this perspective, number marking is the area to focus on noun morphology and exclusively on the recognition of number system in Thali nouns. The analysis of linguistic systems including grammar, lexicon, and phonology provide sound justifications of number marking systems in languages of the world (Chohan & García, 2019).
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".