Bibliographic record
Abstract
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.033 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".