Studying Dictionary Use Among the Law Graduates in Pakistan: A Lexicographic Inquiry
Bibliographic record
Abstract
Specialized Lexicography as it stands today owes much to the user’s centred approach while dictionary making. The present study empirically investigates the use of law dictionaries by the law graduates in Pakistan. The study has taken account of dictionary usage among the Pakistani law graduates within the framework of Function Theory of Lexicography. The study being descriptive qualitative research is placed within pragmatic paradigm. A sample of six hundred law students, who were non-native learners of English and learning this language in ESP context, was drawn and responses were recorded through data tools i.e., questionnaire, semi-structured protocols and observation as favoured in other studies of the similar nature mostly outside Pakistan. Data were analysed through SPSS and presented in tables and graphs. The analysis of qualitative data, however, is based on thematic approach. The study revealed that that existing law dictionaries do not cater to the decoding as well as encoding language needs of the learners although they were found more in need to consult lexicographical resources in law studies because of the complex nature of legal discourse. The study also revealed that the law graduates sadly lacked in awareness regarding both the right choice of law dictionaries best suited to address their potential needs and skills to exploit the dictionary (ies) in order to retrieve the required information successfully. They preferred to use monolingual, bilingual law dictionaries, general purpose dictionaries (hard copy and digital) along with the online resources as a good combination to solve their language problems. Moreover, their reference skills were found weak which may be improved through explicit instructions on dictionary use. The study is part of a doctoral research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".