Bibliographic record
Abstract
Many criminal cases are unresolved despite police possessing evidence of the suspect’s deoxyribonucleic acid (DNA). Historically, searches for matches of DNA samples would be exhausted after running the suspect’s sample through the federal database collected pursuant to the DNA Identification Act. Although the Act permits officers to retrieve DNA samples from convicted criminals and search these databases for matches, it currently prohibits the tactic known as familial searching. In short, such searches tell police whether the suspect DNA sample is connected via familial status to anyone else in a DNA database. This can drastically narrow the pool of potential suspects, allowing police to resort to more traditional investigative methods to ultimately match the suspect’s DNA. Yet, with the expansion of DNA services to the public through websites such as Ancestory.com and 23andme, and open source DNA match sites such as GEDMatch and FamilyTreeDNA, the federal DNA database no longer provides the only means by which law enforcement may test DNA matches. Although data held by private entities will require prior judicial approval before disclosure will be permitted, open-source sites remain available to the public, including police. Following in the footsteps of police practice in the United States, Canadian police have recently begun to search open-source sites to provide leads in cold cases. This development prompts the question of whether and, if so, how such searches ought to be regulated by section 8 of the Canadian Charter of Rights and Freedoms.
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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
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".