“Your wall cannot divide us”: Graffiti in Cyprus and insights into conflict-affected landscapes
Bibliographic record
Abstract
Graffiti in conflict-affected settings offers alternative understandings of local experiences and international challenges that intertwine with everyday routines and spaces. Urban walls deliver canvases to write, tag, and paint to express grievances and aspirations for more peaceful futures, illuminate societal concerns, and offer solidarity on issues that sit within and outside the confines of historical and present-day division. In this expanded visual essay, we explore the publicly available resource of graffiti to gain insights into the challenges and priorities of Cyprus’ conflict-affected landscape. Drawing on observations on both Greek-Cypriot and Turkish-Cypriot sides of the United Nations Buffer Zone, we explore the ways in which graffiti provides space to recognise alternative voices in a society where official and media discourses remain characterised by language of difference and political division. Insights gained through walking surveys conducted in June 2019 were augmented by discussions with local experts to further contextualise the observed graffiti content. We demonstrate the potential value for academics, policymakers, and practitioners of analysing the languages, symbols, and messages of graffiti. We conclude that this initial exploration establishes graffiti as more than ‘vandalism’ and expands our knowledge of conflict-affected landscapes as an indicator of the everyday and the interactions, priorities, and spatial politics of local people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".