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التعددیة الثقافیة وحقوق الأقلیات بین النجاح والإخفاق: دراسة فی فلسفة ویل كیملیكا

2022· article· ar· W4311078439 on OpenAlexaboutno aff
هبة البدوى محمد حمایة حمایة

Bibliographic record

Venueمجلة بحوث کلية الآداب جامعة المنوفية · 2022
Typearticle
Languagear
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

ملخص البحثيعد ويل كيمليكا (William Kymlicka ) ( 1962) من أبرز المفكرين السياسيين الكنديين المعاصرين، يدور هذا البحث حول إشكالية رئيسة هي: ما الذي يقصده ويل كيمليكا بالتعددية الثقافية وما الدور الذي يمكن أن تقوم به للدفاع عن حقوق الأقليات؟، ولمعالجة هذه الإشكالية يتم طرح عدة تساؤلات أهمها: ما المقصود بالتعددية الثقافية؟، ولماذا تم الاهتمام بها في هذه الآونة الأخيرة؟، ما هي حقوق الأقليات وكيف يجب أن يتم التعامل معها؟، ما هي الإنجازات التي تحققت في ظل التعددية الثقافية؟، ماهي العقبات التي تقف حائلاً أمام تحقيق أمال وتطلعات التعددية الثقافية وحقوق الأقليات؟، ما هو مستقبل التعددية الثقافية هل سيكون مصيرها إلى النجاح والتقدم أم إلى التراجع والإخفاق؟.AbstractWilliam Kymlicka (1962) is one of the most prominent contemporary Canadian political thinkers,This research revolves around a main problem: What does Will Kymlicka mean by cultural pluralism and what role can it play to defend the rights of minorities? In order to address this problem, several questions are raised, the most important of which are: What is meant by cultural pluralism? The latter?, What are the rights of minorities and how should they be dealt with? What are the achievements made in light of cultural pluralism? What are the obstacles that stand in the way of achieving the hopes and aspirations of cultural pluralism and minority rights? What is the future of cultural pluralism? Success and progress or decline and failure?

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0860.054

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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