Reading socio-political and spatial dynamics through graffiti in conflict-affected societies
Bibliographic record
Abstract
This paper argues that graffiti can provide a form of socio-political commentary at the local level, and is a valuable, yet often overlooked, resource for scholars and policymakers in conflict-affected societies. Graffiti, in its many forms, can provide rich insight into societies, cultures, social issues, trends, political discourse, and spatial and territorial identities and claims. Thus, this, paper suggests that graffiti is a valuable source of knowledge in societies undergoing social and political transformation, to hear the voices of those often left out from the official discourses. Despite advances in the field of arts and international relations and the focus on the local and the everyday, peace and conflict scholarship and policy still lack systematic engagement with arts-based contributions and how to read them. The paper attempts to address this gap by outlining four core dimensions to consider when attempting to interpret and decode graffiti: the spatial, temporal, political economic and representative dimensions. This can also be viewed as an inquiry into the where, when, who and what. These four elements make up an analytical guide and enable scholars to better understand graffiti, and its political meaning and messaging.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".