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Record W435701220 · doi:10.3138/9781442681354

The Genetic Imaginary: DNA in the Canadian Criminal Justice System

2004· book· en· W435701220 on OpenAlexaboutno aff
Neil Gerlach

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2004
Typebook
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal justiceThe ImaginaryPolitical scienceCrime controlEconomic JusticeCivil libertiesLawEmpowermentCriminal procedureLaw and economicsCriminologySociologyPoliticsPsychology

Abstract

fetched live from OpenAlex

DNA testing and banking has become institutionalized in the Canadian criminal justice system. As accepted and widespread though the practice is, there has been little critique or debate of this practice in a broad public forum on the potential infringement of individual rights or civil liberties. Neil Gerlach's The Genetic Imaginary takes up this challenge, critically examining the social, legal, and criminal justice origins and effects of DNA testing and banking. Drawing on risk analysis, Gerlach explains why Canadians have accepted DNA technology with barely a ripple of public outcry. Despite promises of better crime control and protections for existing privacy rights, Gerlach's examination of police practices, courtroom decisions, and the changing role of scientific expertise in legal decision-making reveals that DNA testing and banking have indeed led to a measurable erosion of individual rights. Biogovernance and the biotechnology of surveillance almost inevitably lead to the empowerment of state agent control and away from due process and legal protection. The Genetic Imaginary demonstrates that the overall effect of these changes to the criminal justice system has been to emphasize the importance of community security at the expense of individual rights. The privatization and politicization of biogovernance will certainly have profound future implications for all Canadians.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.236
Teacher spread0.217 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations21
Published2004
Admission routes1
Has abstractyes

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