Learning from Looking in “Red Clouds”: Lee Maracle’s See as (Re)Creative and Critical Method
Bibliographic record
Abstract
ABSTRACT: In her foundational collection Memory Serves, Stó:lō activist and scholar Lee Maracle explains how “humans bring intent with their vision. Intentions are sometimes dangerously reactionary. We may choose to see phenomena from the angle of perception we inherit. In our conscious state, we may seek that which is beyond our realm of perception, and this produces a visionary perception that can be transformative” (54). This essay explores what it might mean to reflect critically on the ways we (as readers) look at Indigenous works, characters, and creators and on the ways they look back at us. It asks how readers and, specifically, literary critics, might respond meaningfully to Maracle’s calls for looking more deeply, with an attention to intention and positionality. The approach adopted herein builds on settler scholar Sarah Henzi’s identification of the unique potential of Indigenous comics to “speak beyond [the] gaps” (24). It considers how Indigenous comics can promote a unique focus on intersubjective relationality, by reading a case study text—Jen Storm (Ojibway) and Natasha Donovan’s (Métis) “Red Clouds”—through the lens of Maracle’s methodology of see.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".