Archival interventions: Anti‐violence and social justice work in community contexts
Bibliographic record
Abstract
ABSTRACT This panel invites participants and panelists to consider together how archives and other information institutions might work with and engage communities experiencing ongoing and extreme (neo)colonial violence and oppression. We begin from a perspective that suggests that community and/or autonomous archives that reflect community perspectives and histories may indeed have the potential to support the efforts of these same communities to grapple with complex and violent (neo)colonial histories and experiences and to re‐story dominant narratives that serve to stigmatize and marginalize them. This panel explores both the possibilities and limitations of antiviolence archival interventions from a number of angles, including: interrogating the role of the archivist in community archiving; reflecting on how partnerships can be built between archival institutions and communities; and considering how anti‐violence, anti‐racist, decolonizing, and feminist theoretical frameworks can aid archival interventions that speak to the efforts of communities aimed at overcoming structural violence and erasure. Drawing on the archival experiences and practice of panelists, this panel poses a series of questions to the audience to generate discussions aimed at drawing connections between relevant theories, and practical and technical considerations in the service of anti‐violence archiving.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.027 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".