Justice Institute of British Columbia 2013/2014
Bibliographic record
Abstract
It's like a scene from any modern crime television show.In a lab at JIBC's New Westminster campus, law enforcement analysts are huddled over tables strewn with tools, laptops, microscopes and other specialized equipment used to examine evidence.In this case, it's a collection of cellphones.Some phones still work, while others are inoperable for one reason or another: they've been thrown from a tall building, dropped into water, or smashed to destroy incriminating evidence.Today, it's the analysts' goal to learn how to retrieve vital information from these phones, a skill that could ultimately help them solve a crime, find loved ones, or save a life.JIBC has partnered with various agencies and organizations to provide cutting-edge training for public safety professionals in B.C. and around the world.In the case of learning about cellphone repair and forensic analysis, JIBC has partnered with TEEL Technologies Canada.The company is owned by Bob Elder, a retired detective from the Victoria Police Department and a Special Constable with the Saanich Police Department.Elder is an expert in getting information from cellphones, GPS units, hard drives, cameras and other portable storage devices.But cellphones are the most ubiquitous device.According to Statistics Canada, nearly 78% of Canadians are connected with a cellphone.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.311 | 0.103 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".