The Pragmatics of English-Persian Dictionaries: Problems and Solutions
Bibliographic record
Abstract
Inspired by one of the most important contributions modern linguistics has made toward lexicography, the present study is an attempt to investigate the practice of the three most frequently used English-Persian dictionaries in incorporating pragmatic information. To this end, 101 pragmatically marked English words were randomly selected and the treatment of the bilingual dictionaries toward them was closely examined. The study revealed that although labeling is the most frequently used policy, it is still far from desirable as a strategy. The deficiencies can be attributed to the inadequate rate of labeling and inaccuracy and inconsistency in the use of labels. Besides, the study suggests incorporating pragmatic information in the translation equivalents through reducing the number of them to the ones which are good representatives of the specifications of the English word both semantically and pragmatically. Finally, as pragmatic translation equivalents can not be always established, the use of other strategies such as pragmatic examples and usage notes are also suggested.
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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 | 0.059 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".