<b>Transformative Media: A Critical Approach to Alt-right Media Appropriations</b>
Bibliographic record
Abstract
This paper distinguishes between several key features of transformative alternative left media and regressive alt-right media. In the exploration of alt-right media tactics, we map eight cooptation strategies by the alt-right that we categorise into three forms: (1) ideological discursive appropriation; (2) reversal capture; and (3) commodity cooptation. We contrast these strategies with transformative alt-left media practices rooted in intersectional justice and social movements, showing how offline organising, collective care, and fact-based storytelling protect movements from toxic disinformation. This work is vital to spot and disrupt alt-right appropriation and infiltration, fortify intersectional, truth-driven media, and mobilise authentic grassroots power. We argue for the enduring significance of offline organisation in transformative intersectional alternative media practices, where supporting strong relationships potentiates community building, deep affective labour, and positive social transformation.Acknowledgement: This article has been previously published in German by Forschungsjournal Soziale Bewegungen in 2025 as „Transformative Medien: Alternative Medien rutschen von links nach rechts”, https://doi.org/10.1515/fjsb-2025-2006. The original English version is here published with permission by Forschungsjournal Soziale Bewegungen.The Media Action Research Group is supported in part by funding from the Social Sciences and Humanities Research Council of Canada.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.016 | 0.099 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".