Semantic Change in Urdu: A Case Study of “Mashkoor”
Bibliographic record
Abstract
Languages are dynamic in nature and Urdu language is no exception. This study aims to probe semantic change in Urdu lexis and focuses on the meaning of the word “mashkoor” (thanked). For this study, Urdu dictionaries, a corpus of 25 million Urdu words and a questionnaire have been used. Our analysis determines that “mashkoor” has shifted meanings from being “thanked” to “thankful”. The results depict that the grammarians, lexicographers or the teachers are not the authority to decide correct usage in a language but it is the prerogative of users as well. The present study strengthens the idea that Urdu language has changed with the passage of time. It also proposes that Urdu dictionaries should be corpus based and include the current usage by the masses to incorporate the latest changes. This study will serve for other researchers as a springboard to further explore the other aspects of Urdu language.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".