Fun for real! dismantling the “cool colonizer” through the decolonial politics of memes from Kalaallit Nunaat
Bibliographic record
Abstract
In this article, we suggest that memes produced by Kalaallit Inuit (Indigenous people of Greenland), hereafter, Kalaallit, social media actors can be understood as educative decolonial roadmaps, which expose narrative key elements of Danish colonialism as it is experienced in Kalaallit Nunaat—the Indigenous name for Greenland—such as, Nordic exceptionalism, colonial amnesia, discovery tales, the post-colonial imaginary, and the so-called impossibility of Kalaallit sovereignty. Drawing on memes by meme artists: Julie Edel Hardenberg, xoxolilichemnitz, Inunnguaq Reimer, and Jacob Larsen, we ask what forms of humour and playfulness are at play, and how these devices contribute to changing conversations by offering new modes of self-scrutiny in colonial audiences and by introducing decolonial options within the Kalaallit community. Overall, this article sheds light on the anti-colonial work of Kalaallit social media artists and their political decolonizing endeavours in alliance with Indigenous meme-making on a global scale.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".