The social uses of DNA in the political realm or how politics constructs DNA technology in the fight against crime
Bibliographic record
Abstract
Research has shown that the adoption and integration of new technologies in professional environments and daily lives depend less on their objective characteristics and “real” performance than on representations and hopes built into those technologies. This paper will focus on DNA technology and the meanings and expectations invested into it by actors who participated in the debate surrounding two bills on DNA identification in Canada. Through this process, we will uncover the symbolic conditions that allowed for the introduction of the National DNA Databank as a crime-fighting tool: first, the minimization of the power of the substance and the idealization of the DNA databank potentialities; second, the scientification and professionalization of the police through DNA; and third, the reconciliation of Canada's two identities, that of the criminal justice innovator and human rights defender. Those are some of the key symbolic elements that made the creation and expansion of the DNA databank possible.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.097 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".