Creating Ethical Research Partnerships – Relational Accountability in Action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.959 | 0.644 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.928 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.820 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it