Two-Spirit archives and social memory: community, indigeneity and queerness in the archives
Bibliographic record
Abstract
Two-Spirit people have often been the subjects and victims of archives and archival power. However, efforts toward decolonization and changing ideas in archives have the potential to disrupt this narrative. In this thesis I look at the ways institutional or mainstream archives can work with community groups – specifically Two-Spirit communities – to create effective and lasting partnerships to preserve social memory in a western archival format. I begin by sharing the words of several Two-Spirit people who have spoken about what that identity means to them as a way to emphasize what non-Two-Spirit archivists do not know and cannot know about what being Two-Spirit means. This necessary knowledge also serves as the basis to discuss some of the other implications for archiving Two-Spirit records. I then profile four Queer archives, ONE, ArQuives, Lesbian Herstory Archive and rukus! to provide context for how these archives come together, what their relationships with institutions are like and what some of the causes for collecting are. Last, I discuss practical ways for mainstream or institutional archives to build effective partnerships with Two-Spirit communities using the Two-Spirit Archives at the University of Winnipeg Archives as a case study. I use Michelle Caswell’s survivor-centred approach as a way to offer suggestions for archives wanting to build these kinds of relationships and evaluate the success of the Two-Spirit Archives at the University of Winnipeg.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.041 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".