Second Person Pronouns as Person Deixis in Bengali and English: Linguistic Forms and Pragmatic Functions
Bibliographic record
Abstract
Second person pronouns functioning as person deixis are found to be used in both Bengali and English language to express the role relationships as well as the interpersonal relationships involved between the participants in conversation. However, the expression of these relationships through the use of second person deixis varies significantly in the two languages as it necessarily involves both linguistic as well as social aspects. Being an Asian language, Bengali has a detailed and somewhat complex system of encoding the role relationship of the participants, their interrelationships, their social status, level of formality and politeness involved, and so on by the use of second person deixis. In contrast, English, a European language, exhibits relatively simple and straight forward ways of encoding the aforementioned issues of conversation. Based on the intuitive observation of the utterances of the two languages, the present study intends to make a comparative analysis of the use of second person deixis in Bengali and English with a view to exploring the extent to which the two languages differ linguistically and pragmatically in their encoding of social information with the use of such deictic expressions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".