TRANSPHOBIC MEMES IN THE QUEBEC ALTERNATIVE NEWS INDUSTRY
Bibliographic record
Abstract
In this paper, we explore transnational discursive campaigns that use online popular culture to consolidate a “countercultural” vision of hard to extreme right politics. Hyperpartisan sites form a “mirror universe” or alternate news industry, counterposing their passionate partisanship against what they deem the fraudulent objectivity of mainstream media. In our preliminary study, we noted that “anti-woke” hashtags and memes were heavily freighted with transphobic images and messages and co-occurred with a range of other far-right content (with strong currents of anti-immigration, misogynist, “anti-system,” and conspiracy themes). Presented in mocking tones and mobilized by hyperpartisan sites, transphobic memes participate in consolidating far-right hegemony via online countercultural forms. In mapping the distribution patterns of this content, we observed 1) the extent to which a range of seemingly “independent” sites distribute the same content within a short period of times; 2) how “anti-woke” hashtags and memes served to consolidate a far-right worldview and package it as countercultural; 3) and finally, how transphobic content came to constitute something of a “federating” theme – an ideological entry point into or representative of a larger ensemble of sociopolitical arguments. In this sense, transphobia works as a federating meme, creating information cascades that promote alt-right ideologies. Information cascades describe patterns of online conformity (Lemieux 2003) on social media platforms like Twitter. By tracking a range of Quebecois right-wing influenceurs, we aim to ascertain the opportunistic mobilisation and reach of transphobic content in this Quebecois alternative influence network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".