Bibliographic record
Abstract
Often seen as suspect and untrustworthy, gossip as it is currently conceptualized comes from historic attempts by people who have experienced social marginalization to share information, build stronger relationships, and assess a dominant narrative against lived experience. In this article, I will be outlining how gossip has animated my archival work at the Crista Dahl Media Library and Archives, an artist-run centre in Vancouver, BC, and using the Crista Dahl Media Library and Archives as a case study. Several distinct uses of gossip emerge: these include offering space for archives workers to connect and build solidarity, opening up new avenues for reassessing what we consider to be relevant information in archival description, providing strategies for navigating sensitive information within collections, and acting as an alternative to narratives of trauma when considering archival silences. Drawing on practice theory and studies of community archives and deeply influenced by an ethos of transformative justice, this project is connected to the growing body of scholarly work that examines information and memory work through the lens of affect theory and a feminist ethics of care. This work contributes to the articulation of person-centred archival praxis by theorizing gossip as a tactic of care that trains the ear to better notice the experiences, complaints, and contributions of the people surrounding the records at hand.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".