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Record W3168876899 · doi:10.1177/26338076211014569

Criminology: Some lines of flight

2021· article· en· W3168876899 on OpenAlexaff
Julie Berg, Clifford Shearing

Bibliographic record

VenueJournal of Criminology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAnthropoceneCyberspaceSociologyHistoryEnvironmental ethicsComputer scienceThe Internet

Abstract

fetched live from OpenAlex

The 40th Anniversary Edition of Taylor, Walton and Young’s New Criminology, published in 2013, opened with these words: ‘The New Criminology was written at a particular time and place, it was a product of 1968 and its aftermath; a world turned upside down’. We are at a similar moment today. Several developments have been, and are turning, our 21st century world upside down. Among the most profound has been the emergence of a new earth, that the ‘Anthropocene’ references, and ‘cyberspace’, a term first used in the 1960s, which James Lovelock has recently termed a ‘Novacene’, a world that includes both human and artificial intelligences. We live today on an earth that is proving to be very different to the Holocene earth, our home for the past 12,000 years. To appreciate the Novacene one need only think of our ‘smart’ phones. This world constitutes a novel domain of existence that Castells has conceived of as a terrain of ‘material arrangements that allow for simultaneity of social practices without territorial contiguity’ – a world of sprawling material infrastructures, that has enabled a ‘space of flows’, through which massive amounts of information travel. Like the Anthropocene, the Novacene has brought with it novel ‘harmscapes’, for example, attacks on energy systems. In this paper, we consider how criminology has responded to these harmscapes brought on by these new worlds. We identify ‘lines of flight’ that are emerging, as these challenges are being met by criminological thinkers who are developing the conceptual trajectories that are shaping 21st century criminologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0120.061
Scholarly communication0.0210.028
Open science0.0030.008
Research integrity0.0120.020
Insufficient payload (model declined to judge)0.0100.002

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 head0.098
GPT teacher head0.300
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations28
Published2021
Admission routes1
Has abstractyes

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