From Research to Memes: The Enduring Stereotypes of Upernavimmiutut
Bibliographic record
Abstract
Kalaallit Nunaat has a tradition of broadcasting a New Year’s show on KNR-TV to mock the past year and the national political scandals. The show often parodies people from different towns, imitating their dialect. Irvine and Gal’s (2000) concept of processes of language ideologies (iconization, erasure, and fractal recursivity,) alongside Agha’s enregisterment (2003) shows how a dialect can become more marginalized by using features with negative connotations. The article analyses how the media is a tool to reproduce stereotypes and contribute to maintaining the power structures in society by using normalized humour that mocks dialects. The Upernavimmiutut dialect is used as a case study. Coloniality is still visible in today’s Kalaallit Nunaat, and it affects marginalized groups. Standardization and mediatization (Androutsopoulos 2014) also play a significant role in maintaining the power structures in the linguistic arena. The article looks at how people in power have reproduced language ideologies over the decades and how democratized new media maintain them by distributing them through research, TV, and memes in social media.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".