Attitudes and Preferences of Advanced Learners Towards Siraiki Dictionaries
Bibliographic record
Abstract
This study investigates the attitudes and preferences of advanced learners of the Siraiki language towards Siraiki dictionaries available in Pakistan. Descriptive in nature, the study is quantitative approach and has employed survey questionnaire as tool of data collection. The main objectives of the study are: a) to identify the attitudes of advanced learners of the Siraiki language towards using a dictionary for learning a language, b) to identify the types of dictionaries that advanced learners of the Siraiki language prefer most and c) to identify the preferences of advanced learners of the Siraiki language towards their dictionaries and to identify their hurdles and problems towards dictionary use. The subjects of this study comprised 230 advanced learners of Siraiki (138 male and 92 female) from 18 to 24 years of age. The number of respondents at graduate level was 212 and 18 at masters’ level respectively. The subjects of the study were selected through purposive sampling technique. This study reported that 58 out of 230 respondents owned dictionaries. Majority of the respondents reported that dictionary use was a time-consuming task. Siraiki dictionaries were found deficient in organizing lexemes in canonical form, provision of collocations and definitions. Most of the respondents used dictionaries for meaning, followed by pronunciation, spelling, grammar, examples and notes on usage notes respectively. All the students were willing on getting training on dictionary use.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".