Creating Ethical Research Partnerships – Relational Accountability in Action
Bibliographic record
Abstract
Research that focuses on Indigenous street gangs is primarily derived from the experiences and expertise of individuals who work in the criminal justice system or community-based organizations and not street gang members themselves (Grekul & LaRocque, 2011). The primary reason for this is that it is difficult to build research relationships with individuals who, for the majority of their lives, have tried to keep their lives hidden from those who they consider as outsiders. However, it is these narratives of those who have been directly involved with street gangs that provide the greatest insight into what attracts individuals to join, the realities of street gang life, and what is needed to support individuals to exit street gangs. The current article examines how relational accountability framed within the 4Rs (Kirkness & Barnhardt, 1991) was used to engage in a photovoice research project that focused on how Indigenous male ex-gang members came to construct their notions of masculinity within local street gangs. To engage the men in the research, relationships were built with STR8 UP, a community-based gang intervention program located in Saskatoon, Saskatchewan. By building relationships, the foundational components to Indigenous research, trust between researcher and participants was established where modifications within the research methods could occur to engage the men’s participation more fully. The current article also examines the importance of critical reflexivity within relational accountability, as it provides researchers with a tool to understand their social privileges and how this can impact the research process
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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 | 0.185 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.067 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.004 | 0.053 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".