Bibliographic record
Abstract
Abstract Purpose – To assess the potential significance of the gravesites of Canadian residential schools to criminology. Methodology/Approach – The current state of criminological theory with respect to crimes against humanity committed by the state is assessed, particularly with reference to any insights it may offer on the gravesites. Findings – Denunciation of crimes against humanity is the one facet of successful prosecutions that would have value for residential school survivors. The current state of criminological theory for crimes by the state against humanity is inadequate for analyzing how and why those crimes are committed by democratic countries. The capacity of prosecutions by themselves to address the underlying social problems that fuel human rights abuses is limited. There is a need to explore how multi-faceted resolutions can both provide accountability for crimes against humanity and pursue long-standing solutions against further human rights abuses. Originality/Value – Gaps in criminology with respect to analyzing crimes against humanity committed by the state that are in need of further exploration and study are identified. There is a need to develop methodologies for analyzing crimes against humanity committed by democracies. Further study would have significance not only for Indigenous peoples, but also more broadly for racial minorities who are victimized in democracies. Denunciation of crimes against humanity is the only realistic benefit of prosecution. There is therefore a need to explore multi-faceted and enduring resolutions that are not limited to punishment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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".