The Masculine and The Feminine in Arabic and the Illusion of the Unnecessary Proliferation of Words
Bibliographic record
Abstract
This research investigates masculine and feminine in Arabic, aiming at highlighting the formal and semantic limits set by Arab linguists for each of them. Two further issues constitute the aim of this research: the first: the reality of the formal indicators of feminization, and the extent of their conformity with reality, The second is to explain the cause of the existence of these formal signs and to refute the idea of classical linguists that they came to restrict the proliferation of words. This research aims to clarify these elements associated with the idea of femininity in Arabic, and to indicate whether the masculine was originally feminine or not? And spot some formal masculinization as signs of femininity. The research reached a set of results, most notably that the formal signs of the feminine were not strict to the extent adopted by the classical Arab linguists; some Arabic signs of masculinization are not addressed by the classical linguists. It also concluded that the issue of unnecessary proliferationof words was not a suitable cause for the presence of signs of femininity, as the language is not incapable of generating words, nor burdened by it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".