Australian and Canadian far-right extremism: a cross-national comparative analysis of social media mobilisation on Facebook
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Australia and Canada have experienced a growth in far-right extremist activity, from organised hate rallies in urban centres to a growing presence on social media platforms such as Facebook. A common sentiment shared across these movements using social media is the need to defend national and Western identity and culture from what adherents argue are the threat of unchecked immigration, liberal government and the proliferation of Islam. Recent studies have described this ideological narrative within the European and North American context. Little is known, however, about how this is being experienced in the Australian context and how the Australian experience compares to the Canadian far-right extremist movement. This research will conduct a cross-national comparative survey of the online activity and thematic content of the Australian and Canadian far-right extremist movements on Facebook. Any thematic content that emerges from the findings will be interpreted using Social Mobilisation Theory. This conceptual framework seeks to identify some of the ways Australian and Canadian far-right extremist groups mobilise on the social media platform Facebook. This research demonstrates empirically that Australian and Canadian far-right extremist groups share ideological and behavioural commonalities and differences in online activity, thematic content, and in their use of passive and active online behaviour in the form of platform-based symbolic gestures of emotional sentiment. Understanding these similarities and differences in online behaviour provides insight on how far-right groups in these countries evolve online, mobilise on social media, and how best to counter them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it