Bibliographic record
Abstract
Since the release of the Truth and Reconciliation Commission’s (TRC) Calls to Action in 2015, there has not been any research pertaining to their uptake in law schools and justice programs across Canada. This study involved a literature review and an environmental scan of 17 law schools and 25 justice-related programs across Canada and examined their respective curriculums to determine whether and how they have shown consideration for the TRC’s 28th and 63rd Calls to Action. It was posited that, if lawyers and law enforcement workers are not being educated on Indigenous issues while they are in school then they will be more prone to negative interactions with Indigenous clients and individuals. Ultimately, this could contribute to the significant overrepresentation of Indigenous persons in custody in the Canadian justice system. The findings indicated that there is indeed a lack of consideration for the Calls to Action - more so for justice programs than law schools but apparent in both nonetheless. Further research will be required to explore this issue in depth, including what barriers prevent programs and schools from instituting the Calls to Action, and to ensure that academic institutions are contributing to reconciliation efforts and making tangible steps towards change.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 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".