Osgoode Innocence Project students Emily Baier (2L) and Samantha Skinner (3L) win first place ($24K) and third place ($14K) respectively in national letter writing competition administered by Innocence Canada and sponsored by Kent Legal
Bibliographic record
Abstract
The national letter writing competition, administered by Innocence Canada and sponsored by Kent Legal, is designed to increase awareness and advocacy of wrongful convictions through law student engagement.\nContest entrants were required to produce a compelling letter to reasonably persuade the Prime Minister of Canada to enact legislation to create an independent framework for reviewing wrongful convictions. In addition, students were tasked with outlining their advocacy campaign to increase awareness of wrongful convictions.\nInnocence Canada (formerly the Association in Defence of the Wrongly Convicted or AIDWYC) is a Canadian, non-profit organization that was founded in 1993 and incorporated in 2000. Its mandate is to identify, advocate for, and exonerate individuals who have been convicted of a crime they did not commit and to prevent wrongful convictions through legal education and reform.\nSince its inception, Innocence Canada’s team of volunteers has reviewed hundreds of cases, leading to the successful exoneration of more than 22 innocent individuals who together spent more than 190 years in prison for crimes they did not commit. Innocence Canada’s team of pro bono lawyers is currently reviewing approximately 80 claims of innocence.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.355 | 0.091 |
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".