Introduction: The Emotional Dynamics of Backlash Politics beyond Anger, Hate, Fear, Pride, and Loss
Bibliographic record
Abstract
The activists from March for England, a group that had worked closely, although not always seen eye to eye, with the English Defence League, for some years the UK’s most prominent anti-Muslim protest movement, gathered outside Brighton station. It was an excellent day for a St. George’s day parade: warm spring sunshine, just a light breeze. The activists, many wearing, wrapped in or carrying England flags, greeted one another, and shared a joke and a drink as their talk turned to the day ahead. The marchers enacted and expressed a range of emotions. There was evident excitement and anxiety as they discussed parade logistics. They expected a degree of opposition from anti-fascist groups: There always was in Brighton. For some, this was part of the attraction. Yet March for England had only managed to muster a small group today—150 or so—including a number of families and some marchers with limited mobility. There were also, as might be expected, expressions of national pride, felt most intensely during lustily sung renditions of “God Save the Queen” and “England ‘til I die.” National pride mixed with personal pride, appreciation of and respect for their fellow marchers: for being the people who had made the effort to be there and were willing to march despite the anticipated opposition. These feelings were however infused with and accentuated through other emotions and affects of loss, disappointment, embarrassment, and shame even, that in England today so few people seemed to celebrate St. George’s day. Some activists spoke enviously of other countries, such as the United States, France, and Ireland, where they perceived national days to be more widely and joyfully celebrated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".