Let us not drift: Indigenous justice in an age of reconciliation
Bibliographic record
Abstract
At the turn of the 21st century, truth commissions arose as a new possibility to address the violence and trauma of removing Indigenous children from their families and nations in what is now known as North America. The creation of two truth and reconciliation commissions in Canada and Maine marked an important step in addressing Indigenous demands for justice and the end of harm, alongside Indigenous calls for truth-telling. Holding Indigenous conceptions of justice at its core, this dissertation offers a comparative tracing of the work of the Truth and Reconciliation Commission of Canada (2009-2015) and the Maine Wabanaki-State Child Welfare Truth and Reconciliation Commission (2013-2015) as they investigated state practices of removing Indigenous children from their homes and nations. More specifically, this dissertation examines the ways these truth commissions have intersected with Indigenous stories and how Indigenous stories can inform how we understand the work of truth and reconciliation commissions as they move to provide a form of justice for our communities. Within both commission processes, stories of Indigenous experiences in residential schools and the child welfare system were drawn from the perceived margins of settler colonial society in an effort to move towards truth, healing, reconciliation and justice. Despite this attempted inclusion of stories of Indigenous life experiences, I argue that deeply listening to Indigenous stories ¬¬in their various forms—life/ experiential stories, and traditional stories—illuminates the ways that the practice of reconciliation has become disconnected from Indigenous understandings of justice. As such, I argue that listening to Indigenous stories, not just hearing the words but instead taking them to heart, engaging with them and allowing them to guide us, moves toward more informed understandings of what justice looks like for Indigenous communities.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.041 | 0.061 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".