Bibliographic record
Abstract
We arrived late. We were doctoral students who took up as our study the “public secret” (Taussig, 1999, pg. 2) behind the contemporary disappearance of Indigenous women from the midst of the Western Canadian cities in which they, and we, were living. Suzanne Vail was there before we were, having arrived in 1987. She is the protagonist of Katherine Govier’s novel Between Men, a young historian obsessively studying the 1889 murder of a young Cree woman named Rosalie in Calgary, Alberta. Reading Between Men in 2010, we found ourselves anticipated in form and obsession. That Suzanne Vail is a fiction and we are nonfiction does nothing to quiet this shock; rather, it prompts us to engage (with) her as we think through crises of ontology and epistemology in relation to what haunts contemporary efforts to frame historical remembrance of “settling” the Canadian West as a time of conquest (over land and people) and nation-building, a time of progress and development. In this article, we argue that Suzanne’s obsession with Rosalie in Between Men can help us understand just how we, as scholars, are implicated in these contests over history, and explore why this might matter as we struggle toward something that might resemble justice for murdered or missing Indigenous women in the present.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.087 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".