A Survey Study of the Dictionary Use Sub-strategies of English Majors in Saudi Arabia: Dictionary Related Aspects
Bibliographic record
Abstract
This study explored the sub-strategies Saudi English majors use most when consulting the dictionary. In particular, it looked at the aspects of the dictionary use strategy relevant to the dictionary itself rather than the lookup words (mainly purposes for consulting the dictionary, means of dictionary ownership and type of dictionary consulted). The participants were 90 English major students enrolled in an English undergraduate program at the Department of European Languages at King Abdulaziz University, Saudi Arabia. A survey questionnaire adapted from the literature was used to collect data for the study. The results showed that the learners’ strategic preferences were largely affected by the features they liked (e.g. free dictionaries, the ease of use and search as well as portability of tech-based digital dictionaries) or disliked (e.g. the difficulty of search and use in paper dictionaries as well as their heavy weight and high thickness) most about dictionaries. Thus, they preferred to either download dictionary apps to their phones from application stores or go online whenever they needed to consult a dictionary for a word. Moreover, in terms of dictionary types, learners favored the bilingual English-Arabic dictionary (language-wise), dictionary apps and online dictionaries (medium-wise) and the ordinary dictionary (content-wise). Also, they consulted the dictionary no more than five times a day and tended to look up more words when consulting tech-based (digital) dictionaries than when using paper dictionaries. Finally, they used their dictionaries mainly to understand new words while reading.
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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 | 0.001 | 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) | 0.002 | 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".