The Semantic Shift of Some Arabic Lexemes in Egypt after January 25 Revolution
Bibliographic record
Abstract
This study attempts to defend the claim that politics is a linguistically constituted activity, and to show that the terms that inform political beliefs and behavior have historically mutable meanings that have undergone changes related to real political events. Namely, these terms correspond to the experiences which package the semantic material into them .i.e. verbal and situational context yield the shift in meaning. This issue is, however, much more complex and it requires a truly integrating approach, where morphological and semantic criteria are all relevant, as well as psycholinguistic considerations (holistic storage and processing), and sociolinguistic and pragmatic factors. To support the claim above, this study aims to explore the semantic change of some Arabic words after January 25 Revolution in Egypt. It is an attempt to trace evidences of semantic shift in words used during and after the January 25 Revolution through the Egyptian newspapers and social sites. It is concerned with terms used by the pro and anti-revolution activists to name themselves and their opponents. Through this study, the researcher expects that the findings may be useful to enrich the knowledge about the semantic change in Egyptian Arabic in general and to give a better understanding of the meaning shifts and changes that occurred to some lexemes as a result of January 25 Revolution in particular. Moreover, the result of this study is hoped to be used as reference and comparison to other studies in order to make better analysis for further research in semantic change (broadening, Narrowing, Amelioration, Pejoration, Weakening, and Semantic Shift). The study, then, is limited to the purpose of analyzing the semantic changes. The analysis of the collected data represents the semantic change in some Arabic words after January 25 Revolution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".