Using Literature Review to Inform an Anti-Oppressive Approach to Community Safety
Bibliographic record
Abstract
Literature review is a common piece of any scholarly research, but it is rare for it to be squarely at the centre of a community-based project. In this Report from the Field, the research team critically reflects upon the creation and use of a literature review on grassroots, anti-oppressive approaches to community safety in Winnipeg, Manitoba, Canada. Writing in conversation with one another, we explore the tensions of navigating and challenging safety discourse and securitization practices in our city, from our distinct experiences and positions as an academic researcher, community partner, and student research assistant. In the process, we illuminate a collaboration that offers insights for academic and community researchers alike. We reflect on creating an accessible basis for community conversation and planning while doing justice to the sources of anti-oppressive theory and practice, particularly when initially speaking to mostly white and privileged community members. The literature review has generated discussion about what it means to approach safety as a collective resource rather than an exclusive possession, and will inform practical strategies in the neighbourhood and beyond.
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 | 0.311 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.072 | 0.032 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".