Bibliographic record
Abstract
Ever since the start of the Syrian Civil War in 2011, Europe has experienced a huge influx of refugees, which has led to demographic and social changes and created fears about the erosion of the European civilisation and Christian values. The Continent has also experienced several Islamic terrorist attacks- like those in Paris, London, Brussels and Berlin. This, coupled with the rise of the right-wing in Europe, has led to increasing xeno-racism, particularly of an Islamophobic variety that has resulted in the creation of an environment of intolerance and exclusion. At times this has manifested itself as outright hostility towards the Muslim community through hate crimes which take the form of physical and verbal attacks on visibly identifiable and more tangible symbols of Islam like hijabs, headscarves, burkhas and mosques. Yet, most of these hate crimes remain unreported and unaddressed. The European Union (EU) is a one of a kind post-modern entity professing values of equality, democracy and human rights. Given this commitment, this paper attempts to take stock of the EU’s response to rising xeno-racism with particular attention to Islamophobia and the Member States’ attempts to grapple with the same.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| 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".