Bibliographic record
Abstract
The purpose of this study was to validate one of the most frequently used tools for assessing anxiety associated with foreign languages. The researcher translated it into Arabic because there was no Arabic literature on such an instrument. To achieve the goal, a committee approach was followed (Brislin, 1980) to ensure the validity of the translation. A sample of 102 students was purposefully selected from International Islamic University Malaysia. The instrument consists of 33 items to measure communication apprehension, test anxiety, and fear of negative evaluation. A Principal Component Analysis (PCA) was run to validate the instrument; initially, the PCA produced ten-factor solutions, accounting for 71% of the total variance explained. However, the last nine factors had only two or three loadings each, and they had cross-loading as well. Therefore, only one factor was used in the final, which accounted for 51% of the total variance. The researcher took this factor due to its importance for the Arabic literature, which is also in need of a valid instrument to measure communication apprehension. Due to the lack of convergent validity in the other items, the researcher suggests validating the original instrument.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".