Out With the Hero: How TikTok Everyday Stories Are Re-writing the Arctic
Bibliographic record
Abstract
With the rapid growth of TikTok in the last few years, we have seen the emergence of global influencers from diverse backgrounds, whose popularity is enhanced by TikTok’s specific content-based algorithm. In North America, the meta-hashtag #NativeTikTok has become a sharing space for a diverse Indigenous online community. Among these, several young Inuit women have acquired a large fanbase, allowing them to display their culture to a vast public, as well as to bring awareness to issues relating to the Arctic. In this article, we analyze how TikTok became a scale-shifting media for contemporary self-affirmation and displaying of Inuit culture. Drawing data from a case study of six Inuit influencers and an online thematic analysis of their content, we discuss definitions of Inuit authenticity on digital screenscapes, before presenting an analysis of content shared by young Inuit influencers to better understand specific forms of storytelling on TikTok and tensions pertaining to authentic cultural self-presentation. We argue that the TikTok platform provides an efficient tool for young Inuit women to engage with, learn about, and display their culture in their own terms, self-presenting as diverse and modern, in contrast with colonial Inuit imageries.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".