Tendency or Trend? The Direction Towards Modern Latin-Like Arabic Script
Bibliographic record
Abstract
The past few decades have witnessed an aesthetic trend in the Arabic Writing System and its well-known calligraphic arts, which have exploited features of other writing systems, including Latin and Chinese scripts. Although there are great differences between almost every aspect of the Arabic and Latin scripts, this trend has blended certain characteristics of Arabic script with some features of Latin script. This study examines this trend and its experiments and transitions, from the moment it first emerged until the present day. It investigates the motivations underpinning the trend and analyzes its artistic and linguistic characteristics, in which the researcher visually analyzes all possible details and disassembles both orthographic items and calligraphic features into their basic essential scripts. The findings reveal an aesthetic and linguistic trend that is substantial and significant, based on linguistic, cultural, and sociocultural factors, including increased levels of communication, culturalism, advances in technology, transportation, migration, and globalization. Script tools and features are used to divide the main trend into three sub-trends: 1) Script switching, where scripts are interchanged at word-level; 2) Script fusion, where scripts are altered at letter-level; and 3) Faux fonts, which dissolve certain features of Arabic script to mirror Latin script. All of the techniques used to make Arabic script match Latin script have been shown to be culturally-induced and linguistically informative, rather than merely aesthetic. The findings of this study also indicate that this new phenomenon is likely to be in the early stages, with further developments expected to unfold in future.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".