Cultural Challenges Facing Turkey’s Membership in the European Union
Bibliographic record
Abstract
Turkey is one of the first countries asking for membership in the European Union but has not been able to achieve this goal. Even today the prospect of its membership is vague. Turkey hopes to join the EU in its 100th anniversary of independence in 2023 and has set its strategies and policies in line with this goal. It is an Islamic country which might challenge the relatively homogeneous culture of the EU. Some European countries consider the EU a community with a homogeneous cultural, social and political principles and values and regard the orientalist policy of Turkey as an evidence for its heterogeneity with the values existing in the European societies. The present article attempts to investigate the cultural and social barriers as the main obstacle to Turkey’s membership in the EU. Erdogan also believes that cultural problems are the most important obstacle to his country’s presence in the EU. Turkey has only three alternatives if it cannot become an EU member: active involvement, becoming a bridge between East and West, and being an ordinary neighbor to Europe. If Turkey becomes a member of the EU, it can play an important and influential role in the union as the only Islamic member. The present article uses constructivism as its theoretical background and takes it for granted that Turkey faces serious challenges in its path to membership in the EU due to cultural and identity problems.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".