Applying Cognitive Linguistics to Enhance the Semantics of English at: An Experimental Study (Baghdad University)
Bibliographic record
Abstract
The current study is quantitative by nature; it cognitively studies the polysemous network of the English preposition at and its various meanings. The results of the pre-test conducted by the researcher have tentatively revealed that Iraqi second language (L2) learners fall in the perplexity because of the multi-usages of this preposition. This incomprehensive view of the preposition at motivates the researcher to analyze this preposition semantically according to insights from cognitive linguistics (CL) that was developed by Evans and Tyler (2003). Accordingly, sixty-eight second year university students participated in this experimental study. The pre-test and post-test data were analyzed using SPSS. Results have shown the following: First, a progress of more than (0.05) has been detected as far as students' understanding of the multiple usages of the preposition at. Second, the results of the questionnaire have shown a prominent positive change in the students' attitude toward CL approach. Third, the main source of difficulty regarding the diversity in the semantics of the preposition at has been displayed. Fourth, CL as an approach has proven its effectiveness in accurately comprehending of the semantics of the English preposition at.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".