Cognitive Comprehension of “Beyond & Behind”: An Experimental Study of Baghdad University
Bibliographic record
Abstract
The present study analyzes two locative English prepositions behind and beyond from the cognitive semantic point of view. These prepositions pose a problem experienced by Iraqi undergraduates. The complexity of these two prepositions encourages the researcher to use the cognitive linguistics (CL) approach and its insights as developed by Evans and Tyler 2003 to test its validity and help the Iraqi students. The data analysis is quantitatively-based. Seventy second-year university students participate in this experimental study. The pre-test and post-test data are analyzed through SPSS statistical editor and the results show a progress of more than (0.05≤). The results of the questionnaire show a noticeable positive change in the students’ attitude toward CL approach and display the main source of difficulty that is related to perplexity in the usage of these prepositions. The effectiveness of CL approach in getting accurate comprehension of the English prepositions behind and beyond is proved by the results of this experiment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".