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Record W2278861428 · doi:10.23962/10539/19314

Revolution, Graffiti and Copyright: The Cases of Egypt and Tunisia

2015· article· en· W2278861428 on OpenAlexfundno aff
Nagla Rizk

Bibliographic record

VenueThe African Journal of Information and Communication (AJIC) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
FundersFP7 International CooperationDeutsche Gesellschaft für Internationale ZusammenarbeitBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungInternational Development Research Centre
KeywordsGraffitiAncient historyHistoryPolitical scienceArtVisual arts

Abstract

fetched live from OpenAlex

During and after the Arab uprisings in 2011, there was an outburst of creative production in Egypt and Tunisia, serving as a means to counter state-controlled media and to document alternative narratives of the revolutions. One of the most prominent modes of creative output was graffiti. Within an access to knowledge (A2K) framework that views graffiti as an important knowledge good, this article outlines the author's findings from research into perspectives towards revolutionary graffiti held by graffiti artists and graffiti consumers in Egypt and Tunisia. The main quest of this work is to identify a copyright regime best suited to the priorities of both the revolutionary graffiti artists and the consumers of this art, cognisant also of the possibilities offered by increasingly widespread use of, and access to, online digital platforms. The research looked at how artists and consumers relate to the revolutionary graffiti, how they feel about its commercialisation, and how they feel about the idea of protecting it with copyright. Based on the research findings, the author concludes that an A2K-enabling approach to preservation and dissemination of the revolutionary graffiti - and an approach that would best cater to the needs of both the artists and the consumers - is provided by the Creative Commons (CC) suite of flexible copyright licences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.293
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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