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Record W3155765367

Graffiti and Criminal Law: War or Peace? How Does New York City’s Zero-Tolerance Approach Regulate Graffiti in Comparison to the Liberal Approach Taken by the City of Toronto?

2021· article· en· W3155765367 on OpenAlexaboutno aff
Alexander Merkulov

Bibliographic record

VenueSSRN Electronic Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
Fundersnot available
KeywordsGraffitiPresumptionJurisdictionLawPolitical scienceSociologyArtVisual arts
DOInot available

Abstract

fetched live from OpenAlex

Graffiti is a rather controversial concept in modern days. It has managed to divide contemporary society into a bipartisan reality. One can either favour graffiti and be of the opinion that it is capable of being classified as artistic work and as a result susceptible to recognition and admiration or be completely against it and advocate that graffiti is an evil that instigates the further commission of a crime. What is interesting to note is that these approaches can be found during the analysis of the regulatory frameworks of different jurisdictions that cover graffiti. It can be seen that some jurisdictions adopt a zero-tolerance policy by criminalising graffiti, and everything related to it and going as far as rewarding citizens who turn in graffiti artists. New York City serves as an excellent example of such jurisdiction. On the other side of the spectre is the liberal approach that albeit is not necessarily in favour of the presence of graffiti on the streets, still makes an attempt to consider the interests of graffiti artists at least to some extent. An example of such jurisdiction is the city of Toronto. The Toronto Municipality starts from the presumption that unsolicited graffiti is to be regarded as vandalism. However, this presumption is rebuttable should the owner of the wall request a specific administrative body for reconsideration. This paper aims to address the 2 approaches that jurisdictions can take in relation to graffiti works. In doing so it will attempt to find an answer to the following question: How does New York City’s zero-tolerance approach regulate graffiti in comparison to the liberal approach taken by the city of Toronto? In doing so, the first step is to clarify the sufficiently confusing terminology in the area of graffiti. After that, the graffiti problem as such will be explained, namely how it is viewed and what are the ideas governing its opponents and proponents. Additionally, the motives of graffiti artists will be explained. This will then be followed by a comparative overview of the approaches taken by the 2 above-mentioned jurisdictions.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.538
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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
Published2021
Admission routes1
Has abstractyes

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