Getting to Resurgence Through Sourcing Cultural Strength: An Analysis of Robertson’s Will I See and LaPensée’s Deer Woman
Bibliographic record
Abstract
Many Indigenous and non-Indigenous peoples contend that the Canadian government has failed Indigenous peoples in addressing the crisis of missing and murdered Indigenous women and girls. This paper examines how two Indigenous-authored graphic novels—Deer Woman by Elizabeth LaPensee, Will I See by David A. Robertson et al.—circumvent the so-far minimal efforts of the Canadian government to respond to the completed Missing and Murdered Indigenous Women and Girls Inquiry, which itself experienced multiple delays in completion, by having their Indigenous female protagonists source strength from within their communities and cultures to “act-out” against targeted violence. I argue that in doing so, the protagonists enact resurgence, as defined by Leanne Simpson as an event that occurs through strength sourced from within Indigenous contexts. I then examine how resurgence is further practiced in the texts through conveying complex felt knowledges to readers, and through peopling the texts with missing and murdered Indigenous women. While pre-existing work has examined the ways in which Indigenous-authored comics reflect Indigenous spirituality and orature (Dudek) and Indigenous storytelling (Tiger), my paper is novel in considering the reflections of the authors on the crisis of missing and murdered Indigenous women and girls, and in examining the ways in which the graphic novels themselves depict and enact resurgence as a response.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.035 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".