Implementing Call to Action 93 for survivors of colonial violence: An exploration of positive peace for Ukrainians displaced by war to Manitoba
Bibliographic record
Abstract
In response to the final report of the Truth and Reconciliation Commission of Canada (TRCC), the federal government of Canada has committed to all of the 94 Calls to Action contained in that report. Call to Action 93 focuses specifically on newcomers to Canada stating: “We call upon the federal government, in collaboration with the national Aboriginal organizations, to revise the information kit for newcomers to Canada and its citizenship test to reflect a more inclusive history of the diverse Aboriginal peoples of Canada, including information about the Treaties and the history of residential schools” (TRCC, 2015). Using a systematic review methodology, this study examines the existing literature for insight into how to best implement Call to Action 93 for war-affected Ukrainians displaced to Manitoba as a result of the on-going invasion of the nation of Ukraine by the Russian Federation. Ukrainian settlers and Indigenous people in Manitoba share a long history of complex relationships dating back to the first wave of Ukrainian immigration in the late 1800s. Ukrainian and Indigenous peoples share common experiences with colonization and social marginalization. Based on the findings of the systematic review, this study presents a project proposal on how to effectively implement Call to Action 93. The goal is to encourage Ukrainian survivors of colonial violence to empathize with Indigenous people’s colonial experiences in Canada and identify healing pathways from colonial trauma while staying true to the original intention of the call.
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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".