Aspects Marking in English and Thali: A Contrastive Study
Bibliographic record
Abstract
The purpose of the paper is to explore the aspect system in Thali, a language spoken in Thal region, including district Layya, Bhakar and neighboring areas of Jhang, in Punjab province by a large number of people. This research paper presents comparisons and contrasts between Thali aspect system and English aspect system. There are only two aspects in Thali, namely, perfect and progressive. Perfect aspect can be categorized into past perfect and present perfect in terms of time dimensions. Similarly, progressive aspect is also categorized into past progressive and present progressive from time dimensions. All types of aspects in Thali are morphologically marked but aspect system in English is different by using morphological marking as well as several complex constructions like have + past participle, be + present participle, and have + been + present participle for perfect, progressive and perfect progressive, respectively. Thali has only four structures for aspect whereas English has 17 different types of aspectual structures described in examples (24–40). The analyses and data examined in the paper are basically drawn from the native speaker intuitions and grammar (Beames, 1872–79). It is really a challenging job for Thali learners to conceptualise these different structures. As a final point, this paper finds out EFL issues and proposes some pedagogical strategies for teaching and learning English aspect system as a foreign language to Thali EFL learners.
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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 | 0.005 |
| 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 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".