Being (in)formed by indigenous voices: First steps to using graphic narratives to decolonise speculative fiction
Bibliographic record
Abstract
The Greenlandic visual artist Nuka K. Godtfredsen and his literary and scientific collaborators have produced a series of four graphic narratives to represent distinct moments in Greenland's history, spanning the pre-colonised and colonial period. These narratives employ aspects of magic realism and adopt an approach to narrative that focuses on the supernatural and presents modes of being that contrast with their audiences' understanding of realities that are ordinarily (only) visible. I argue that these graphic narratives use strategies from speculative fiction that frame the modern European presence in Greenland and the narrative of colonialism as one of several multiple realities in the Arctic, rather than its central axis, leaving open the possibility for indigenous Greenlanders to speak on their own terms. This enables these graphic narratives to illuminate aspects of knowledge (including features of oral legend and supernatural encounters) that were previously discredited in colonial discourse. Furthermore, I show that attending to how embodied aspects of Greenlandic Inuit storytelling traditions can be captured in the graphic narrative medium may be an effective decolonial strategy, which could be employed by speculative fiction. I thus advocate methodologies for speculative fiction that strategically broaden its boundaries in order to address its intractable colonial legacy. Informed by approaches that focus attention on form - such as Marks's haptic visuality (2000) and visual theories of the power of hand-drawn comics (Groensteen 2010, Chute 2008) to engage the reader/viewer in both an embodied and reflective way - I assert that including graphic narratives which employ strategies of speculative fiction may present a unique opportunity for the genre to mount a powerful challenge to a colonial knowledge production.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".