The Space Between Us: Exploring Colonization And Injustice Through Red: A Haida Manga
Bibliographic record
Abstract
RED: A Haida Manga is an Indigenous comic book based on a traditional Haida narrative, and it was created by Michael Nicoll Yahgulanaas, who is of Haida descent. This major research paper examines RED from three different perspectives: 1) how RED functions as a comic book in terms of its format and structure; 2) how it challenges contemporary ideas of indigeneity presented in the mainstream media; and 3) how it defies genre and reader expectations. These three analyses demonstrate how Yahgulanaas uses the structure, narrative, and artistic style of RED to create a political statement about colonization, as well as a social commentary on injustice faced by Indigenous peoples in Canada. This is mainly done through Yahgulanaas’ use of formlines in place of traditional comic book gutters, the inter-tribal storyline, as well as the combination of Manga and Haida art. These elements work together to illustrate a worldview that focuses on the whole, rather than the individual self, while communicating Yahgulanaas’ “unwavering belief that, beyond differences in Indigenous and Western ways of thinking, people of all backgrounds can find common ground in shared concerns” (Mauzé, 2018, para. 6). RED provides non-Indigenous readers an opportunity to familiarize themselves with the “Indigenous experience in a postcolonial world” (Chavarria, 2009, p. 48), while allowing Indigenous readers to reclaim a sense of identity and autonomy through an authentic representation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".