Insights into the Semantics of Reduplication in English and Arabic
Bibliographic record
Abstract
The paper aims to describe and compare the semantics of reduplication in English and Arabic. The paper shows more semantic similarities in reduplication than differences between both languages; although, Arabic reduplication is noted to be semantically more productive than English reduplication. Both languages divide reduplication into full/partial, free/bound, and continuous/discontinuous. Moreover, both languages share the senses of reduplication like; repetition, emphasis, intensity, onomatopoeia, contempt, affection, plurality, non-uniformity, and instability, nonsense, spread out, scatter, movement, contrast, continuity, completion, and lack of control. The semantic connection was developed between most of these concepts, which showed that ambiguity was common between both languages. Both the languages used reduplication in the nursery rhymes, lyrics, games, prayers, second language teaching, children’s phonics cartoons, advertisements, tongue twisters, slogans, newspaper headlines, and political and ideological rhetoric. These similarities support the belief of some linguists stating that different languages in the world share a variety of ‘universal’ semantic features. The study concluded that Arabic reduplication was semantically more productive than English reduplication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".