Antiviolence and Marginalized Communities: Knowledge Creation, Community Mobilization, and Social Justice through a Participatory Archiving Approach
Bibliographic record
Abstract
The Digital Archives and Marginalized Communities Project (DAMC), at the University of Manitoba, is an interdisciplinary collaboration to design and develop three separate but related digital archives using a participatory archiving approach with stakeholder community groups. Working titles for these collections are the Missing and Murdered Indigenous Women Database (MMIWD), the Sex Work Database (SWD), and the Post-Apology Residential School Database (PARSD). This article discusses research and development from the project’s inception in 2012 through the end of 2014, reflecting on the practical and theoretical considerations that arise for researchers and practitioners in the information science professions as a result of engaging with anticolonial and antiviolence feminist methodologies. These methodological perspectives place the experiences and knowledge of Indigenous and sex worker communities at the center of decolonizing processes, foregrounding the need for archival processes that not only captures but also uses these knowledge(s) as the organizational scaffolding upon which to build socially just and representative archives for specific marginalized communities. Using examples drawn from all three archives, this article demonstrates how the goals, intentions, and knowledges of marginalized communities might be built into digital archives projects through a participatory archiving approach. This discussion is followed by an examination of how fostering and maintaining respectful relationships between all members involved with DAMC collaborations is fundamentally connected to both participatory archiving processes and broader social justice objectives.
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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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.035 | 0.074 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.003 | 0.004 |
| 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".