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Record W2398592833 · doi:10.3968/8442

Arabic Language and Civilization in the Eyes of the European History

2016· article· en· W2398592833 on OpenAlexvenueno aff
Ali Alshhre

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsOrientOrientalismCivilizationIgnoranceOppressionHistoryMiddle EastIdentity (music)ArabicAncient historyLiteraturePhilosophyLinguisticsArtPolitical scienceLawAesthetics

Abstract

fetched live from OpenAlex

Though the direct and indirect effects of the Arabic language in the Middle Ages on some European languages such as Spanish, French, and English, there were little discussion of this fact in their histories, especially in the history of English. The little discussion of this fact is due to many various reasons. First, consideration of the Orient by the Europeans as their ancient domain led to many cultural conflicts between the Arabs and the Europeans historically. The cultural gap between the Oriental and Occidental person for understanding the other. Second, many attempts to occupy the Orient give clear illustrations that the Europeans, especially the British and the French consider the Orient as a place that hold their second identity and origins of their religions, Judaism and Christianity. During the complete colonization of the Middle East in the 19th and 20th centuries make the reader figure out how important the Orient for the Europeans in general and for the British and the French in particular. Then the descriptions of the Arabs as inferior and ignorant make one understand that the practices of some Arab people for polygamy and their oppression of the Arab women are unacceptable in the European cultures and traditions. Therefore, the Arabs are classified as backward, “primitive and slave traders” (Ridouani, 2016, p.2). This paper argues and investigates the cultural reasons regarding the Occident’s ignorance of this fact in their history.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.282
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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