“<i>Voir l'Autre</i>”? Seeing the Other, the Developments of the Arab Spring and the European Neighborhood Policy toward Algeria and Tunisia
Bibliographic record
Abstract
The European Union (EU) is not only affecting European space, but is also trying to spread “prosperity, stability and security” in its immediate geographical surroundings (European Commission. n.d. European Neighborhood Policy. http://ec.europa.eu/world/enp/index_en.htm). Therefore the European Neighborhood Policy (ENP) was developed to address regional differences at the external border in the immediate neighborhood of the EU. To influence beyond its own boundaries, the EU tries to convince the partner countries (the countries addressed through the ENP) through partial inclusion and conditionality. This article will regard the perception of the EU of its geographical neighbors beyond its own border by analyzing official EU documents. As a result of the unexpected developments of the Arab Spring in 2011, the EU needed to adjust its approach towards its neighborhood. This paper will analyze to what extent the Union is acknowledging its “others,” its partner countries before and after the beginning of the Arab Spring. This will be approached according to a concept of Albert Camus who proposed “seeing the other” (voir l'autre) as an option to render conflict unnecessary. Building upon this concept this article also introduces the exploratory concept of “listening to the other” (écouter l'autre).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".