Bibliographic record
Abstract
The developments that are increasingly taking place in the communication and technology scene are indubitably advantageous, but they are also risky. This study looks into an issue that has generated a heated debate among different actors in the Moroccan community from a different perspective though. Besides considering the cultural weight of foreign soft products, which are invading the second widely-viewed channel, 2M, this paper essentially considers the linguistic impact of 2M’s practice of dubbing many occidental broadcasts into the local dialect, Moroccan Arabic (MA). Such dubbing is fostered by the recourse of diverse businesses to MA in their advertising policy by transcribing MA using French and/or Standard Arabic (SA) script. While it is true that customers have already extensively used MA whether in chatting platforms or when dispatching messages over cellulars not equipped with Arabic keyboards, this practice, I strongly believe, is not without any consequences. For, many academicians, such as Abdellah Laroui, are worried that these novel communication behaviors on the part of media networks, companies and consumers endanger SA, cultural heritage and identity. In accordance, I claim that this onslaught waged by Moroccan media avenues alongside firms entails both linguistic and cultural perils for the community. This paper articulates such worries, delineates the implications of both positions and proposes recommendations for redressing the situation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".